international journal of research studies in agriculture

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Linking ISSN (ISSN-L): 2454-6224

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Resource information

Title proper: International journal of research studies in agricultural sciences.

Abbreviated key-title: Int. j. res. stud. agric. sci.

Original alphabet of title: Basic roman

Subject: UDC : 63

Subject: Agriculture and related sciences and techniques. Forestry. Farming. Wildlife exploitation

Publisher: Hyderabad: ARC Publications Private Limited

Dates of publication: 2015- 9999

Frequency: Monthly

Type of resource: Periodical

Language: English

Country: India

Medium: Online

Indexed by: ROAD

Indexed by: FATCAT

Indexed by: WIKIDATA

Indexed by: OPENALEX

Indexed by: CROSSREF

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Type of record: Confirmed

Last modification date: 06/02/2021

ISSN Center responsible of the record: ISSN National Centre for India For all potential issues concerning the description of the publication identified by this bibliographic record (missing or wrong data etc.), please contact the ISSN National Centre mentioned above by clicking on the link.

Record creation date: 16/02/2016

Original ISSN Centre: ISSN National Centre for India

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Integration of remote sensing and machine learning for precision agriculture: a comprehensive perspective on applications.

1. Introduction

2. remote sensing technology and the machine learning method, 2.1. remote sensing data in precision agriculture, 2.2. overview of the use of ml algorithms in precision agriculture, 3. integrated application of remote sensing technology and the machine learning method, 3.1. agricultural monitoring and identification, 3.2. stress detection of diseases and insect pests, 3.3. management and analysis of soil and land, 3.4. prediction and decision making regarding crop yield, 4. discussion, 4.1. current challenges, 4.1.1. acquisition and processing of multi-source rs data, 4.1.2. interpretability and generalization of the model, 4.2. prospects for the future, 4.2.1. trend of intelligence and automation, 4.2.2. data sharing and multidisciplinary interaction, 5. conclusions, author contributions, data availability statement, conflicts of interest.

• Tran, T.-N.-D.; Lakshmi, V. Enhancing human resilience against climate change: Assessment of hydroclimatic extremes and sea level rise impacts on the Eastern Shore of Virginia, United States. Sci. Total Environ. 2024 , 947 , 174289. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tran, T.-N.-D.; Nguyen, B.Q.; Grodzka-Lukaszewska, M.; Sinicyn, G.; Lakshmi, V. The role of reservoirs under the impacts of climate change on the Srepok River basin, Central Highlands of Vietnam. Front. Environ. 2023 , 11 , 1304845. [ Google Scholar ] [ CrossRef ]
• Tran, T.-N.-D.; Tapas, M.R.; Do, S.K.; Etheridge, R.; Lakshmi, V. Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina. J. Environ. Manag. 2024 , 363 , 121375. [ Google Scholar ] [ CrossRef ]
• Tran, T.N.D.; Do, S.K.; Nguyen, B.Q.; Tran, V.N.; Grodzka-Łukaszewska, M.; Sinicyn, G.; Lakshmi, V. Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River Basin Using CMIP6 Projections. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024 , 17 , 7516–7529. [ Google Scholar ] [ CrossRef ]
• Matton, N.; Canto, G.S.; Waldner, F.; Valero, S.; Morin, D.; Inglada, J.; Arias, M.; Bontemps, S.; Koetz, B.; Defourny, P. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sens. 2015 , 7 , 13208–13232. [ Google Scholar ] [ CrossRef ]
• Alavi, M.; Albaji, M.; Golabi, M.; Ali Naseri, A.; Homayouni, S. Estimation of sugarcane evapotranspiration from remote sensing and limited meteorological variables using machine learning models. J. Hydrol. 2024 , 629 , 130605. [ Google Scholar ] [ CrossRef ]
• Sadiq, M.A.; Sarkar, S.K.; Raisa, S.S. Meteorological drought assessment in northern Bangladesh: A machine learning-based approach considering remote sensing indices. Ecol. Indic. 2023 , 157 , 111233. [ Google Scholar ] [ CrossRef ]
• Bellvert, J.; Mata, M.; Vallverdú, X.; Paris, C.; Marsal, J. Optimizing precision irrigation of a vineyard to improve water use efficiency and profitability by using a decision-oriented vine water consumption model. Precis. Agric. 2021 , 22 , 319–341. [ Google Scholar ] [ CrossRef ]
• Yomo, M.; Yalo, E.N.; Gnazou, M.D.-T.; Silliman, S.; Larbi, I.; Mourad, K.A. Forecasting land use and land cover dynamics using combined remote sensing, machine learning algorithm and local perception in the Agoènyivé Plateau, Togo. Remote Sens. Appl. Soc. Environ. 2023 , 30 , 100928. [ Google Scholar ] [ CrossRef ]
• Kumar, M.; Bhattacharya, B.K.; Pandya, M.R.; Handique, B.K. Machine learning based plot level rice lodging assessment using multi-spectral UAV remote sensing. Comput. Electron. Agric. 2024 , 219 , 108754. [ Google Scholar ] [ CrossRef ]
• Kganyago, M.; Adjorlolo, C.; Mhangara, P.; Tsoeleng, L. Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture. Comput. Electron. Agric. 2024 , 218 , 108730. [ Google Scholar ] [ CrossRef ]
• Petrović, B.; Bumbálek, R.; Zoubek, T.; Kuneš, R.; Smutný, L.; Bartoš, P. Application of precision agriculture technologies in Central Europe-review. J. Agric. Food Res. 2024 , 15 , 101048. [ Google Scholar ] [ CrossRef ]
• Mana, A.A.; Allouhi, A.; Hamrani, A.; Rehman, S.; el Jamaoui, I.; Jayachandran, K. Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices. Smart Agric. Technol. 2024 , 7 , 100416. [ Google Scholar ] [ CrossRef ]
• Brewster, C.; Roussaki, I.; Kalatzis, N.; Doolin, K.; Ellis, K. IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot. IEEE Commun. Mag. 2017 , 55 , 26–33. [ Google Scholar ] [ CrossRef ]
• Shuai, L.; Li, Z.; Chen, Z.; Luo, D.; Mu, J. A research review on deep learning combined with hyperspectral Imaging in multiscale agricultural sensing. Comput. Electron. Agric. 2024 , 217 , 108577. [ Google Scholar ] [ CrossRef ]
• Diaz-Gonzalez, F.A.; Vuelvas, J.; Correa, C.A.; Vallejo, V.E.; Patino, D. Machine learning and remote sensing techniques applied to estimate soil indicators. Review Ecol. Indic. 2022 , 135 , 108517. [ Google Scholar ] [ CrossRef ]
• El-Omairi, M.A.; El Garouani, A. A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data. Heliyon 2023 , 9 , e20168. [ Google Scholar ] [ CrossRef ]
• Kasampalis, D.A.; Alexandridis, T.K.; Deva, C.; Challinor, A.; Moshou, D.; Zalidis, G. Contribution of Remote Sensing on Crop Models: A Review. J. Imaging 2018 , 4 , 52. [ Google Scholar ] [ CrossRef ]
• Tran, T.-N.-D.; Nguyen, B.Q.; Zhang, R.; Aryal, A.; Grodzka-Lukaszewska, M.; Sinicyn, G.; Lakshmi, V. Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam. Remote Sens. 2023 , 15 , 1030. [ Google Scholar ] [ CrossRef ]
• Tran, T.-N.-D.; Le, M.-H.; Zhang, R.; Nguyen, B.Q.; Bolten, J.D.; Lakshmi, V. Robustness of gridded precipitation products for vietnam basins using the comprehensive assessment framework of rainfall. Atmos. Res. 2023 , 293 , 106923. [ Google Scholar ] [ CrossRef ]
• Tran, T.-N.-D.; Nguyen, Q.B.; Vo, N.D.; Marshall, R.; Gourbesville, P. Assessment of Terrain Scenario Impacts on Hydrological Simulation with SWAT Model. Application to Lai Giang Catchment, Vietnam. In Advances in Hydroinformatics ; Springer: Singapore, 2022; pp. 1205–1222. [ Google Scholar ]
• Aryal, A.; Tran, T.-N.-D.; Kumar, B.; Lakshmi, V. Evaluation of Satellite-Derived Precipitation Products for Streamflow Simulation of a Mountainous Himalayan Watershed: A Study of Myagdi Khola in Kali Gandaki Basin, Nepal. Remote Sens. 2023 , 15 , 4762. [ Google Scholar ] [ CrossRef ]
• Mani, P.K.; Mandal, A.; Biswas, S.; Sarkar, B.; Mitran, T.; Meena, R.S. Remote Sensing and Geographic Information System: In A Tool for Precision Farming ; Mitran, T., Meena, R.S., Chakraborty, A., Eds.; Geospatial Technologies for Crops and Soils; Springer: Singapore, 2021; pp. 49–111. [ Google Scholar ]
• Carneiro, F.M.; Filho, A.L.d.B.; Ferreira, F.M.; Junior, G.d.F.S.; Brandão, Z.N.; da Silva, R.P.; Shiratsuchi, L.S. Soil and satellite remote sensing variables importance using machine learning to predict cotton yield. Smart Agric. Technol. 2023 , 5 , 100292. [ Google Scholar ] [ CrossRef ]
• Morlin Carneiro, F.; Angeli Furlani, C.E.; Zerbato, C.; Candida de Menezes, P.; da Silva Gírio, L.A.; Freire de Oliveira, M. Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors. Precis. Agric. 2020 , 21 , 979–1007. [ Google Scholar ] [ CrossRef ]
• Ai, B.; Wen, Z.; Jiang, Y.C.; Gao, S.; Lv, G.N. Sea surface temperature inversion model for infrared remote sensing images based on deep neural network. Infrared Phys. Technol. 2019 , 99 , 231–239. [ Google Scholar ] [ CrossRef ]
• Zhang, W.H.; Sun, L.; Lian, L.S.; Yang, Y.K. MODIS Aerosol Optical Depth Inversion Over Urban Areas Supported by BRDF/Albedo Products. J. Indian Soc. Remote Sens. 2020 , 48 , 1345–1354. [ Google Scholar ] [ CrossRef ]
• Aires, F.; Pellet, V. Estimating Retrieval Errors from Neural Network Inversion Schemes—Application to the Retrieval of Temperature Profiles From IASI. IEEE Trans. Geosci. Remote Sens. 2021 , 59 , 6386–6396. [ Google Scholar ] [ CrossRef ]
• Liu, B.; Liu, L.; Tian, L.; Cao, W.; Zhu, Y.; Asseng, S. Post-heading heat stress and yield impact in winter wheat of China. Glob. Change Biol. 2014 , 20 , 372–381. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Akter, N.; Rafiqul Islam, M. Heat stress effects and management in wheat. A review. Agron. Sustain. Dev. 2017 , 37 , 37. [ Google Scholar ] [ CrossRef ]
• Wójtowicz, M.; Wójtowicz, A.; Piekarczyk, J. Application of remote sensing methods in agriculture. Commun. Biometry Crop Sci. 2016 , 11 , 31–50. [ Google Scholar ]
• Skendžić, S.; Zovko, M.; Lešić, V.; Pajač Živković, I.; Lemić, D. Detection and Evaluation of Environmental Stress in Winter Wheat Using Remote and Proximal Sensing Methods and Vegetation Indices—A review. Diversity 2023 , 15 , 481. [ Google Scholar ] [ CrossRef ]
• Kumar, A.S.; Reddy, A.M.; Srinivas, L.; Reddy, P.M. Assessment of Surface Water Quality in Hyderabad Lakes by Using Multivariate Statistical Techniques, Hyderabad-India. Environ. Pollut. 2015 , 4 , 4. [ Google Scholar ] [ CrossRef ]
• Odermatt, D.; Danne, O.; Philipson, P.; Brockmann, C. Diversity II water quality parameters from ENVISAT (2002–2012): A new global information source for lakes. Earth Syst. Sci. Data. 2018 , 10 , 1527–1549. [ Google Scholar ] [ CrossRef ]
• Shang, P.; Shen, F. Atmospheric Correction of Satellite GF-1/WFV Imagery and Quantitative Estimation of Suspended Particulate Matter in the Yangtze Estuary. Sensors 2016 , 16 , 1997. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014 , 92 , 79–97. [ Google Scholar ] [ CrossRef ]
• Lee, C.-J.; Yang, M.-D.; Tseng, H.-H.; Hsu, Y.-C.; Sung, Y.; Chen, W.-L. Single-plant broccoli growth monitoring using deep learning with UAV imagery. Comput. Electron. Agric. 2023 , 207 , 107739. [ Google Scholar ] [ CrossRef ]
• Marques, T.; Carreira, S.; Miragaia, R.; Ramos, J.; Pereira, A. Applying deep learning to real-time UAV-based forest monitoring: Leveraging multi-sensor imagery for improved results. Expert Syst. Appl. 2024 , 245 , 123107. [ Google Scholar ] [ CrossRef ]
• Bah, M.D.; Hafiane, A.; Canals, R. Weeds detection in UAV imagery using SLIC and the hough transform. In Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, Montreal, QC, Canada, 28 November–1 December 2017; pp. 1–6. [ Google Scholar ]
• Yang, M.-D.; Huang, K.-S.; Kuo, Y.-H.; Tsai, H.P.; Lin, L.-M. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery. Remote Sens. 2017 , 9 , 583. [ Google Scholar ] [ CrossRef ]
• Yang, Q.; She, B.; Huang, L.S.; Yang, Y.Y.; Zhang, G.; Zhang, M.; Hong, Q.; Zhang, D.Y. Extraction of soybean planting area based on feature fusion technology of multi-source low altitude unmanned aerial vehicle images. Ecol. Inform. 2022 , 70 , 101715. [ Google Scholar ] [ CrossRef ]
• Maimaitijiang, M.; Sagan, V.; Sidike, P.; Hartling, S.; Esposito, F.; Fritschi, F.B. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens. Environ. 2020 , 237 , 111599. [ Google Scholar ] [ CrossRef ]
• Peng, J.B.; Wang, D.L.; Zhu, W.X.; Yang, T.; Liu, Z.; Rezaei, E.E.; Li, J.; Sun, Z.G.; Xin, X.P. Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features. Int. J. Appl. Earth Obs. Geoinf. 2023 , 124 , 103494. [ Google Scholar ] [ CrossRef ]
• Khan, A.; Vibhute, A.D.; Mali, S.; Patil, C.H. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecol. Inform. 2022 , 69 , 101678. [ Google Scholar ] [ CrossRef ]
• Han, W.; Zhang, X.; Wang, Y.; Wang, L.; Huang, X.; Li, J.; Wang, S.; Chen, W.; Li, X.; Feng, R.; et al. A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities. ISPRS J. Photogramm. Remote Sens. 2023 , 202 , 87–113. [ Google Scholar ] [ CrossRef ]
• Coulibaly, S.; Kamsu-Foguem, B.; Kamissoko, D.; Traore, D. Deep learning for precision agriculture: A bibliometric analysis. Intelligent Syst. Appl. 2022 , 16 , 200102. [ Google Scholar ] [ CrossRef ]
• Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine Learning in Agriculture: A review. Sensors 2018 , 18 , 2674. [ Google Scholar ] [ CrossRef ]
• Sarkar, C.; Gupta, D.; Gupta, U.; Hazarika, B.B. Leaf disease detection using machine learning and deep learning: Review and challenges. Appl. Soft Comput. 2023 , 145 , 110534. [ Google Scholar ] [ CrossRef ]
• Miao, Z.H.; Yu, X.Y.; Li, N.; Zhang, Z.; He, C.X.; Li, Z.; Deng, C.Y.; Sun, T. Efficient tomato harvesting robot based on image processing and deep learning. Precis. Agric. 2023 , 24 , 254–287. [ Google Scholar ] [ CrossRef ]
• Fu, Y.; Yang, G.; Pu, R.; Li, Z.; Li, H.; Xu, X.; Song, X.; Yang, X.; Zhao, C. An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives. Eur. J. Agron. 2021 , 124 , 126241. [ Google Scholar ] [ CrossRef ]
• Casagli, N.; Cigna, F.; Bianchini, S.; Hölbling, D.; Füreder, P.; Righini, G.; Del Conte, S.; Friedl, B.; Schneiderbauer, S.; Iasio, C.; et al. Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER. Remote Sens. Appl. Soc. Environ. 2016 , 4 , 92–108. [ Google Scholar ] [ CrossRef ]
• Knoll, F.J.; Czymmek, V.; Poczihoski, S.; Holtorf, T.; Hussmann, S. Improving efficiency of organic farming by using a deep learning classification approach. Comput. Electron. Agric. 2018 , 153 , 347–356. [ Google Scholar ] [ CrossRef ]
• Ouma, Y.O. Advancements in medium and high resolution Earth observation for land-surface imaging: Evolutions, future trends and contributions to sustainable development. Adv. Space Res. 2016 , 57 , 110–126. [ Google Scholar ] [ CrossRef ]
• Sofia, G. Combining geomorphometry, feature extraction techniques and Earth-surface processes research: The way forward. Geomorphology 2020 , 355 , 107055. [ Google Scholar ] [ CrossRef ]
• Saha, A.; Chandra Pal, S. Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends. J. Hydrol. 2024 , 632 , 130907. [ Google Scholar ] [ CrossRef ]
• Rodi, N.S.N.; Malek, M.A.; Ismail, A.R. Monthly Rainfall Prediction Model of Peninsular Malaysia Using Clonal Selection Algorithm. Int. J. Eng. Technol. 2018 , 7 , 182–185. [ Google Scholar ] [ CrossRef ]
• Latif, S.D.; Alyaa Binti Hazrin, N.; Hoon Koo, C.; Lin Ng, J.; Chaplot, B.; Feng Huang, Y.; El-Shafie, A.; Najah Ahmed, A. Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches. Alex. Eng. J. 2023 , 82 , 16–25. [ Google Scholar ] [ CrossRef ]
• Khanal, S.; Fulton, J.; Shearer, S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput. Electron. Agric. 2017 , 139 , 22–32. [ Google Scholar ] [ CrossRef ]
• Ahmed, Z.; Shew, A.; Nalley, L.; Popp, M.; Green, V.S.; Brye, K. An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture. Int. Soil Water Conserv. Res. 2024 , 12 , 77–95. [ Google Scholar ] [ CrossRef ]
• Jafarbiglu, H.; Pourreza, A. A comprehensive review of remote sensing platforms, sensors, and applications in nut crops. Comput. Electron. Agric. 2022 , 197 , 106844. [ Google Scholar ] [ CrossRef ]
• Degerickx, J.; Roberts, D.A.; McFadden, J.P.; Hermy, M.; Somers, B. Urban tree health assessment using airborne hyperspectral and LiDAR imagery. Int. J. Appl. Earth Obs. Geoinf. 2018 , 73 , 26–38. [ Google Scholar ] [ CrossRef ]
• Duan, M.; Wang, Z.; Sun, L.; Liu, Y.; Yang, P. Monitoring apple flowering date at 10 m spatial resolution based on crop reference curves. Comput. Electron. Agric. 2024 , 225 , 109260. [ Google Scholar ] [ CrossRef ]
• Meng, R.; Gao, R.; Zhao, F.; Huang, C.; Sun, R.; Lv, Z.; Huang, Z. Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest. Remote Sens. Environ. 2022 , 269 , 112847. [ Google Scholar ] [ CrossRef ]
• Wu, B.; Liang, A.; Zhang, H.; Zhu, T.; Zou, Z.; Yang, D.; Tang, W.; Li, J.; Su, J. Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning. For. Ecol. Manag. 2021 , 486 , 118986. [ Google Scholar ] [ CrossRef ]
• Zhu, X.; Wang, R.; Shi, W.; Yu, Q.; Li, X.; Chen, X. Automatic Detection and Classification of Dead Nematode-Infested Pine Wood in Stages Based on YOLO v4 and GoogLeNet. Forests 2023 , 14 , 601. [ Google Scholar ] [ CrossRef ]
• Luo, Y.; Huang, H.; Roques, A. Early Monitoring of Forest Wood-Boring Pests with Remote Sensing. Annu. Rev. Entomol. 2023 , 68 , 277–298. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ren, S.; Chen, H.; Hou, J.; Zhao, P.; Dong Qg Feng, H. Based on historical weather data to predict summer field-scale maize yield: Assimilation of remote sensing data to WOFOST model by ensemble Kalman filter algorithm. Comput. Electron. Agric. 2024 , 219 , 108822. [ Google Scholar ] [ CrossRef ]
• Guerrero, N.M.; Aparicio, J.; Valero-Carreras, D. Combining Data Envelopment Analysis and Machine Learning. Mathematics 2022 , 10 , 909. [ Google Scholar ] [ CrossRef ]
• Sharma, A.; Jain, A.; Gupta, P.; Chowdary, V. Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access 2021 , 9 , 4843–4873. [ Google Scholar ] [ CrossRef ]
• Behmann, J.; Mahlein, A.K.; Rumpf, T.; Römer, C.; Plümer, L. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis. Agric. 2015 , 16 , 239–260. [ Google Scholar ] [ CrossRef ]
• Helm, J.M.; Swiergosz, A.M.; Haeberle, H.S.; Karnuta, J.M.; Schaffer, J.L.; Krebs, V.E.; Spitzer, A.I.; Ramkumar, P.N. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Curr. Rev. Musculoskelet. Med. 2020 , 13 , 69–76. [ Google Scholar ] [ CrossRef ]
• Gao, Z.; Luo, Z.; Zhang, W.; Lv, Z.; Xu, Y. Deep Learning Application in Plant Stress Imaging: A Review. AgriEngineering 2020 , 2 , 430–446. [ Google Scholar ] [ CrossRef ]
• Benos, L.; Tagarakis, A.C.; Dolias, G.; Berruto, R.; Kateris, D.; Bochtis, D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021 , 21 , 3758. [ Google Scholar ] [ CrossRef ]
• Choi, R.Y.; Coyner, A.S.; Kalpathy-Cramer, J.; Chiang, M.F.; Campbell, J.P. Introduction to Machine Learning, Neural Networks, and Deep Learning. Transl. Vis. Sci. Technol. 2020 , 9 , 14. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Simeone, O. A Very Brief Introduction to Machine Learning with Applications to Communication Systems. IEEE Trans. Cogn. Commun. Netw. 2018 , 4 , 648–664. [ Google Scholar ] [ CrossRef ]
• Albarakati, H.M.; Khan, M.A.; Hamza, A.; Khan, F.; Kraiem, N.; Jamel, L.; Almuqren, L.; Alroobaea, R. A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024 , 17 , 6338–6353. [ Google Scholar ] [ CrossRef ]
• Finley, A.O.; Andersen, H.E.; Babcock, C.; Cook, B.D.; Morton, D.C.; Banerjee, S. Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska. J. Agric. Biol. Environ. Stat. 2024 , 28 . [ Google Scholar ] [ CrossRef ]
• Shafik, W.; Tufail, A.; Namoun, A.; De Silva, L.C.; Apong, R. A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends. IEEE Access 2023 , 11 , 59174–59203. [ Google Scholar ] [ CrossRef ]
• El Akhal, H.; Ben Yahya, A.; Moussa, N.; El Alaouil, A.E. A novel approach for image-based olive leaf diseases classification using a deep hybrid model. Ecol. Inform. 2023 , 77 , 102276. [ Google Scholar ] [ CrossRef ]
• Abbas, F.; Afzaal, H.; Farooque, A.A.; Tang, S. Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms. Agronomy 2020 , 10 , 1046. [ Google Scholar ] [ CrossRef ]
• Fu, Z.P.; Jiang, J.; Gao, Y.; Krienke, B.; Wang, M.; Zhong, K.T.; Cao, Q.; Tian, Y.C.; Zhu, Y.; Cao, W.X.; et al. Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle. Remote Sens. 2020 , 12 , 508. [ Google Scholar ] [ CrossRef ]
• Guo, H.L.; Zhang, R.R.; Dai, W.H.; Zhou, X.W.; Zhang, D.J.; Yang, Y.H.; Cui, J. Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region. Agronomy 2022 , 12 , 2111. [ Google Scholar ] [ CrossRef ]
• Erler, A.; Riebe, D.; Beitz, T.; Löhmannsröben, H.G.; Gebbers, R. Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR). Sensors 2020 , 20 , 418. [ Google Scholar ] [ CrossRef ]
• Yoon, H.I.; Lee, H.; Yang, J.S.; Choi, J.H.; Jung, D.H.; Park, Y.J.; Park, J.E.; Kim, S.M.; Park, S.H. Predicting Models for Plant Metabolites Based on PLSR, AdaBoost, XGBoost, and LightGBM Algorithms Using Hyperspectral Imaging Brassica juncea . Agriculture 2023 , 13 , 1477. [ Google Scholar ] [ CrossRef ]
• Bakhshipour, A. Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features. IEEE Access 2021 , 9 , 82021–82030. [ Google Scholar ] [ CrossRef ]
• Luo, L.L.; Chang, Q.R.; Wang, Q.; Huang, Y. Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements. Remote Sens. 2021 , 13 , 4560. [ Google Scholar ] [ CrossRef ]
• Shinde, S.; Patidar, H. Hyperspectral Image Classification for Vegetation Detection Using Lightweight Cascaded Deep Convolutional Neural Network. J. Indian Soc. Remote Sens. 2023 , 51 , 2159–2166. [ Google Scholar ] [ CrossRef ]
• Barbedo, J.G.A.; Koenigkan, L.V.; Santos, P.M.; Ribeiro, A.R.B. Counting Cattle in UAV Images—Dealing with Clustered Animals and Animal/Background Contrast Changes. Sensors 2020 , 20 , 2126. [ Google Scholar ] [ CrossRef ]
• Han, T.; Hu, X.M.; Zhang, J.; Xue, W.H.; Che, Y.F.; Deng, X.Q.; Zhou, L.H. Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019. Environ. Pollut. 2023 , 336 , 122334. [ Google Scholar ] [ CrossRef ]
• Gauci, A.; Abela, J.; Austad, M.; Cassar, L.F.; Zarb Adami, K. A Machine Learning approach for automatic land cover mapping from DSLR images over the Maltese Islands. Environ. Model. Softw. 2018 , 99 , 1–10. [ Google Scholar ] [ CrossRef ]
• Idol, T.; Haack, B.; Mahabir, R. Radar speckle reduction and derived texture measures for land cover/use classification: A case study. Geocarto Int. 2017 , 32 , 18–29. [ Google Scholar ] [ CrossRef ]
• Li, L.; Dong, Y.Y.; Xiao, Y.X.; Liu, L.Y.; Zhao, X.; Huang, W.J. Combining Disease Mechanism and Machine Learning to Predict Wheat Fusarium Head Blight. Remote Sens. 2022 , 14 , 2732. [ Google Scholar ] [ CrossRef ]
• Bebie, M.; Cavalaris, C.; Kyparissis, A. Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach. Remote Sens. 2022 , 14 , 3880. [ Google Scholar ] [ CrossRef ]
• Zhou, Y.N.; Luo, J.C.; Feng, L.; Yang, Y.P.; Chen, Y.H.; Wu, W. Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data. GISci. Remote Sens. 2019 , 56 , 1170–1191. [ Google Scholar ] [ CrossRef ]
• Jimenez, A.F.; Ortiz, B.V.; Bondesan, L.; Morata, G.; Damianidis, D. Long Short-Term Memory Neural Network for irrigation management: A case study from Southern Alabama, USA. Precis. Agric. 2021 , 22 , 475–492. [ Google Scholar ] [ CrossRef ]
• Chen, C.; Bao, Y.X.; Zhu, F.; Yang, R.M. Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data. Int. J. Remote Sens. 2024 , 45 , 772–790. [ Google Scholar ] [ CrossRef ]
• Dumdumaya, C.E.; Cabrera, J.S. Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines. Artif. Intell. Geosci. 2023 , 4 , 111–118. [ Google Scholar ] [ CrossRef ]
• Bao Pham, Q.; Ajim Ali, S.; Parvin, F.; Van On, V.; Mohd Sidek, L.; Đurin, B.; Cetl, V.; Šamanović, S.; Nguyet Minh, N. Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network. Adv. Space Res. 2024 , 10 , 29900–29926. [ Google Scholar ] [ CrossRef ]
• Zhang, J.; Zhang, Y.; Zhou, T.; Sun, Y.; Yang, Z.; Zheng, S. Research on the identification of land types and tree species in the Engebei ecological demonstration area based on GF-1 remote sensing. Ecol. Inform. 2023 , 77 , 102242. [ Google Scholar ] [ CrossRef ]
• Belgiu, M.; Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sens. 2016 , 114 , 24–31. [ Google Scholar ] [ CrossRef ]
• Whyte, A.; Ferentinos, K.P.; Petropoulos, G.P. A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms. Environ. Model. Softw. 2018 , 104 , 40–54. [ Google Scholar ] [ CrossRef ]
• Ali, M.Z.; Qazi, W.; Aslam, N. A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier. Egypt J. Remote Sens. Space Sci. 2018 , 21 , S29–S35. [ Google Scholar ] [ CrossRef ]
• Ghayour, L.; Neshat, A.; Paryani, S.; Shahabi, H.; Shirzadi, A.; Chen, W.; Al-Ansari, N.; Geertsema, M.; Pourmehdi Amiri, M.; Gholamnia, M.; et al. Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sens. 2021 , 13 , 1349. [ Google Scholar ] [ CrossRef ]
• Nguyen, T.T.; Ngo, H.H.; Guo, W.S.; Chang, S.W.; Nguyen, D.D.; Nguyen, C.T.; Zhang, J.; Liang, S.; Bui, X.T.; Hoang, N.B. A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm. Sci. Total Environ. 2022 , 833 , 12–155066. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liu, Y.; Sun, Q.; Huang, J.; Feng, H.K.; Wang, J.J.; Yang, G.J. Estimation of Potato Above Ground Biomass Based on UAV Multispectral Images. Spectrosc. Spectr. Anal. 2021 , 41 , 2549–2555. [ Google Scholar ]
• Li, Z.P.; Zhou, X.G.; Cheng, Q.; Fei, S.P.; Chen, Z. A Machine-Learning Model Based on the Fusion of Spectral and Textural Features from UAV Multi-Sensors to Analyse the Total Nitrogen Content in Winter Wheat. Remote Sens. 2023 , 15 , 2152. [ Google Scholar ] [ CrossRef ]
• Pejak, B.; Lugonja, P.; Antic, A.; Panic, M.; Pandzic, M.; Alexakis, E.; Mavrepis, P.; Zhou, N.A.; Marko, O.; Crnojevic, V. Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data. Remote Sens. 2022 , 14 , 2256. [ Google Scholar ] [ CrossRef ]
• Ye, Y.; Huang, Q.Q.; Rong, Y.; Yu, X.H.; Liang, W.J.; Chen, Y.X.; Xiong, S.W. Field detection of small pests through stochastic gradient descent with genetic algorithm. Comput. Electron. Agric. 2023 , 206 , 107694. [ Google Scholar ] [ CrossRef ]
• Zualkernan, I.; Abuhani, D.A.; Hussain, M.H.; Khan, J.; El Mohandes, M. Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey. Drones 2023 , 7 , 382. [ Google Scholar ] [ CrossRef ]
• Khan, S.; Tufail, M.; Khan, M.T.; Khan, Z.A.; Iqbal, J.; Alam, M. A novel semi-supervised framework for UAV based crop/weed classification. PLoS ONE 2021 , 16 , e0251008. [ Google Scholar ] [ CrossRef ]
• Mujkic, E.; Philipsen, M.P.; Moeslund, T.B.; Christiansen, M.P.; Ravn, O. Anomaly Detection for Agricultural Vehicles Using Autoencoders. Sensors 2022 , 22 , 3608. [ Google Scholar ] [ CrossRef ]
• Chen, X.; Zhang, C.; Yan, K.; Wei, Z.; Cheng, N. Risk Assessment of Agricultural Soil Heavy Metal Pollution Under the Hybrid Intelligent Evaluation Model. IEEE Access 2023 , 11 , 106847–106858. [ Google Scholar ] [ CrossRef ]
• Alvarenga, T.C.; De Lima, R.R.; Simao, S.D.; Brandao Junior, L.C.; Bueno Filho, J.S.D.S.; Alvarenga, R.R.; Rodrigues, P.B.; Leite, D.F. Ensemble of hybrid Bayesian networks for predicting the AMEn of broiler feedstuffs. Comput. Electron. Agric. 2022 , 198 , 107067. [ Google Scholar ] [ CrossRef ]
• Lu, Q.K.; Xie, Y.P.; Wei, L.F.; Wei, Z.Y.; Tian, S.; Liu, H.; Cao, L. Extended Attribute Profiles for Precise Crop Classification in UAV-Borne Hyperspectral Imagery. IEEE Geosci. Remote Sens. Lett. 2024 , 21 , 2500805. [ Google Scholar ] [ CrossRef ]
• Maeda, N.; Tonooka, H. Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning. Sensors 2023 , 23 , 210. [ Google Scholar ] [ CrossRef ]
• Furuya, D.E.G.; Ma, L.F.; Pinheiro, M.M.F.; Gomes, F.D.G.; Gonçalvez, W.N.; Marcato, J.; Rodrigues, D.D.; Blassioli-Moraes, M.C.; Michereff, M.F.F.; Borges, M.; et al. Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 2021 , 105 , 102608. [ Google Scholar ] [ CrossRef ]
• Javadi, S.H.; Guerrero, A.; Mouazen, A.M. Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing. Sensors 2022 , 22 , 645. [ Google Scholar ] [ CrossRef ]
• Devarajan, G.G.; Nagarajan, S.M.; Ramana, T.V.; Vignesh, T.; Ghosh, U.; Alnumay, W. DDNSAS: Deep reinforcement learning based deep Q-learning network for smart agriculture system. Sust. Comput. 2023 , 39 , 100890. [ Google Scholar ] [ CrossRef ]
• Din, A.; Ismail, M.Y.; Shah, B.B.; Babar, M.; Ali, F.; Baig, S.U. A deep reinforcement learning-based multi-agent area coverage control for smart agriculture. Comput. Electr. Eng. 2022 , 101 , 108089. [ Google Scholar ] [ CrossRef ]
• García, R.; Aguilar, J.; Toro, M.; Pinto, A.; Rodríguez, P. A systematic literature review on the use of machine learning in precision livestock farming. Comput. Electron. Agric. 2020 , 179 , 105826. [ Google Scholar ] [ CrossRef ]
• Shahab, H.; Iqbal, M.; Sohaib, A.; Ullah Khan, F.; Waqas, M. IoT-based agriculture management techniques for sustainable farming: A comprehensive review. Comput. Electron. Agric. 2024 , 220 , 108851. [ Google Scholar ] [ CrossRef ]
• Rehman, T.U.; Mahmud, M.S.; Chang, Y.K.; Jin, J.; Shin, J. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Comput. Electron. Agric. 2019 , 156 , 585–605. [ Google Scholar ] [ CrossRef ]
• Sladojevic, S.; Arsenovic, M.; Anderla, A.; Culibrk, D.; Stefanovic, D. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Comput. Intell. Neurosci. 2016 , 2016 , 3289801. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, J.; Qiao, Y.; Liu, S.; Zhang, J.; Yang, Z.; Wang, M. An improved YOLOv5-based vegetable disease detection method. Comput. Electron. Agric. 2022 , 202 , 107345. [ Google Scholar ] [ CrossRef ]
• Ashwinkumar, S.; Rajagopal, S.; Manimaran, V.; Jegajothi, B. Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Mater. Today Proc. 2022 , 51 , 480–487. [ Google Scholar ] [ CrossRef ]
• Yu, Y. Research Progress of Crop Disease Image Recognition Based on Wireless Network Communication and Deep Learning. Wirel. Commun. Mob. Comput. 2021 , 2021 , 7577349. [ Google Scholar ] [ CrossRef ]
• Ang, Y.H.; Shafri, H.Z.M.; Lee, Y.P.; Abidin, H.; Bakar, S.A.; Hashim, S.J.; Che’Ya, N.N.; Hassan, M.R.; San Lim, H.; Abdullah, R. A novel ensemble machine learning and time series approach for oil palm yield prediction using Landsat time series imagery based on NDVI. Geocarto Int. 2022 , 37 , 9865–9896. [ Google Scholar ] [ CrossRef ]
• Aydin, Y.; Isikdag, U.; Bekdas, G.; Nigdeli, S.M.; Geem, Z.W. Use of Machine Learning Techniques in Soil Classification. Sustainability 2023 , 15 , 2374. [ Google Scholar ] [ CrossRef ]
• Osco, L.P.; Nogueira, K.; Marques Ramos, A.P.; Faita Pinheiro, M.M.; Furuya, D.E.G.; Gonçalves, W.N.; de Castro Jorge, L.A.; Marcato Junior, J.; dos Santos, J.A. Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery. Precis. Agric. 2021 , 22 , 1171–1188. [ Google Scholar ] [ CrossRef ]
• Kellenberger, B.; Marcos, D.; Tuia, D. Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning. Remote Sens. Environ. 2018 , 216 , 139–153. [ Google Scholar ] [ CrossRef ]
• Kamath, R.; Balachandra, M.; Vardhan, A.; Maheshwari, U. Classification of paddy crop and weeds using semantic segmentation. Cogent Eng. 2022 , 9 , 2018791. [ Google Scholar ] [ CrossRef ]
• Jin, X.; Sun, Y.; Che, J.; Bagavathiannan, M.; Yu, J.; Chen, Y. A novel deep learning-based method for detection of weeds in vegetables. Pest Manag. Sci. 2022 , 78 , 1861–1869. [ Google Scholar ] [ CrossRef ]
• Xun, L.; Zhang, J.; Cao, D.; Wang, J.; Zhang, S.; Yao, F. Mapping cotton cultivated area combining remote sensing with a fused representation-based classification algorithm. Comput. Electron. Agric. 2021 , 181 , 105940. [ Google Scholar ] [ CrossRef ]
• Zhao, H.; Huang, Y.; Wang, X.; Li, X.; Lei, T. The performance of SPEI integrated remote sensing data for monitoring agricultural drought in the North China Plain. Field Crops Res. 2023 , 302 , 109041. [ Google Scholar ] [ CrossRef ]
• Lyu, X.; Li, X.; Dang, D.; Dou, H.; Xuan, X.; Liu, S.; Li, M.; Gong, J. A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing. Ecol. Indic. 2020 , 114 , 106310. [ Google Scholar ] [ CrossRef ]
• Xiao, D.; Niu, H.; Guo, F.; Zhao, S.; Fan, L. Monitoring irrigation dynamics in paddy fields using spatiotemporal fusion of Sentinel-2 and MODIS. Agric. Water Manag. 2022 , 263 , 107409. [ Google Scholar ] [ CrossRef ]
• Zhang, G.; Xiao, X.; Dong, J.; Kou, W.; Jin, C.; Qin, Y.; Zhou, Y.; Wang, J.; Menarguez, M.A.; Biradar, C. Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. ISPRS J. Photogramm. Remote Sens. 2015 , 106 , 157–171. [ Google Scholar ] [ CrossRef ]
• Liu, J.-R.; Liu, Q.; Khoury, J.; Li, Y.-J.; Han, X.-H.; Li, J.; Ibla, J.C. Hypoxic preconditioning decreases nuclear factor κB activity via Disrupted in Schizophrenia-1. Int. J. Biochem. Cell Biol. 2016 , 70 , 140–148. [ Google Scholar ] [ CrossRef ]
• Guo, Y.; Ren, H. Remote sensing monitoring of maize and paddy rice planting area using GF-6 WFV red edge features. Comput. Electron. Agric. 2023 , 207 , 107714. [ Google Scholar ] [ CrossRef ]
• DeVries, B.; Verbesselt, J.; Kooistra, L.; Herold, M. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sens. Environ. 2015 , 161 , 107–121. [ Google Scholar ] [ CrossRef ]
• Jevsenak, J.; Arnic, D.; Krajnc, L.; Skudnik, M. Machine Learning Forest Simulator (MLFS): R package for data-driven assessment of the future state of forests. Ecol. Inform. 2023 , 75 , 102115. [ Google Scholar ] [ CrossRef ]
• Bagheri Bodaghabadi, M.; Martínez-Casasnovas, J.A.; Esfandiarpour Borujeni, I.; Salehi, M.H.; Mohammadi, J.; Toomanian, N. Database extension for digital soil mapping using artificial neural networks. Arab. J. Geosci. 2016 , 9 , 701. [ Google Scholar ] [ CrossRef ]
• Dornik, A.; Drăguț, L.; Urdea, P. Classification of Soil Types Using Geographic Object-Based Image Analysis and Random Forests. Pedosphere 2018 , 28 , 913–925. [ Google Scholar ] [ CrossRef ]
• Lu, H.; Liu, C.; Li, N.; Fu, X.; Li, L. Optimal segmentation scale selection and evaluation of cultivated land objects based on high-resolution remote sensing images with spectral and texture features. Environ. Sci. Pollut. Res. 2021 , 28 , 27067–27083. [ Google Scholar ] [ CrossRef ]
• Rai, N.; Flores, P. Leveraging transfer learning in ArcGIS Pro to detect “doubles” in a sunflower field. In ASABE Annual International Virtual Meeting ; ASABE: St. Joseph, MI, USA, 2021; p. 1. [ Google Scholar ]
• Butte, S.; Vakanski, A.; Duellman, K.; Wang, H.; Mirkouei, A. Potato crop stress identification in aerial images using deep learning-based object detection. Agron. J. 2021 , 113 , 3991–4002. [ Google Scholar ] [ CrossRef ]
• Rong, J.; Zhou, H.; Zhang, F.; Yuan, T.; Wang, P. Tomato cluster detection and counting using improved YOLOv5 based on RGB-D fusion. Comput. Electron. Agric. 2023 , 207 , 107741. [ Google Scholar ] [ CrossRef ]
• Guo, Q.; Potter, K.M.; Ren, H.; Zhang, P. Impacts of Exotic Pests on Forest Ecosystems: An Update. Forests 2023 , 14 , 605. [ Google Scholar ] [ CrossRef ]
• Li, W.; Zheng, T.; Yang, Z.; Li, M.; Sun, C.; Yang, X. Classification and detection of insects from field images using deep learning for smart pest management: A systematic review. Ecol. Inform. 2021 , 66 , 101460. [ Google Scholar ] [ CrossRef ]
• Sun, Y.; Liu, X.; Yuan, M.; Ren, L.; Wang, J.; Chen, Z. Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring. Biosyst. Eng. 2018 , 176 , 140–150. [ Google Scholar ] [ CrossRef ]
• Partel, V.; Nunes, L.; Stansly, P.; Ampatzidis, Y. Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence. Comput. Electron. Agric. 2019 , 162 , 328–336. [ Google Scholar ] [ CrossRef ]
• Mahanta, D.K.; Bhoi, T.K.; Komal, J.; Samal, I.; Mastinu, A. Spatial, spectral and temporal insights: Harnessing high-resolution satellite remote sensing and artificial intelligence for early monitoring of wood boring pests in forests. Plant Stress. 2024 , 11 , 100381. [ Google Scholar ] [ CrossRef ]
• Bhatnagar, S.; Mahanta, D.K.; Vyas, V.; Samal, I.; Komal, J.; Bhoi, T.K. Storage Pest Management with Nanopesticides Incorporating Silicon Nanoparticles: A Novel Approach for Sustainable Crop Preservation and Food Security. Silicon 2024 , 16 , 471–483. [ Google Scholar ] [ CrossRef ]
• Barchenkov, A.; Rubtsov, A.; Safronova, I.; Astapenko, S.; Tabakova, K.; Bogdanova, K.; Anuev, E.; Arzac, A. Features of Scots Pine Mortality Due to Incursion of Pine Bark Beetles in Symbiosis with Ophiostomatoid Fungi in the Forest-Steppe of Central Siberia. Forests 2023 , 14 , 1301. [ Google Scholar ] [ CrossRef ]
• Ballesteros, R.; Ortega, J.F.; Hernández, D.; Moreno, M.A. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: Application to maize and onion crops of a semi-arid region in Spain. Precis. Agric. 2014 , 15 , 593–614. [ Google Scholar ] [ CrossRef ]
• Gopalakrishnan, R.; Subhash, C.; Kalpana, K. Predictive zoning of rice stem borer damage in southern India through spatial interpolation of weather-based models. J. Environ. Biol. 2014 , 35 , 923–928. [ Google Scholar ]
• Nurfaiz Abd Kharim, M.; Wayayok, A.; Fikri Abdullah, A.; Rashid Mohamed Shariff, A.; Mohd Husin, E.; Razif Mahadi, M. Predictive zoning of pest and disease infestations in rice field based on UAV aerial imagery. Egypt. J. Remote Sens. Space Sci. 2022 , 25 , 831–840. [ Google Scholar ] [ CrossRef ]
• Shi, Y.; Huang, W.; Luo, J.; Huang, L.; Zhou, X. Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Comput. Electron. Agric. 2017 , 141 , 171–180. [ Google Scholar ] [ CrossRef ]
• Yuan, L.; Zhang, H.; Zhang, Y.; Xing, C.; Bao, Z. Feasibility assessment of multi-spectral satellite sensors in monitoring and discriminating wheat diseases and insects. Optik 2017 , 131 , 598–608. [ Google Scholar ] [ CrossRef ]
• Ebrahimi, M.A.; Khoshtaghaza, M.H.; Minaei, S.; Jamshidi, B. Vision-based pest detection based on SVM classification method. Comput. Electron. Agric. 2017 , 137 , 52–58. [ Google Scholar ] [ CrossRef ]
• Kumar, D.; Kukreja, V. An Instance Segmentation Approach for Wheat Yellow Rust Disease Recognition. In Proceedings of the International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain, 7–8 December 2021; pp. 926–931. [ Google Scholar ]
• Amarathunga, D.C.; Grundy, J.; Parry, H.; Dorin, A. Methods of insect image capture and classification: A Systematic literature review. Smart Agric. Technol. 2021 , 1 , 100023. [ Google Scholar ] [ CrossRef ]
• Tetila, E.C.; Machado, B.B.; Menezes, G.V.; Belete, N.A.d.S.; Astolfi, G.; Pistori, H. A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests. IEEE Geosci. Remote Sens. Lett. 2020 , 17 , 1837–1841. [ Google Scholar ] [ CrossRef ]
• Abade, A.; Porto, L.F.; Ferreira, P.A.; de Barros Vidal, F. NemaNet: A convolutional neural network model for identification of soybean nematodes. Biosyst. Eng. 2022 , 213 , 39–62. [ Google Scholar ] [ CrossRef ]
• Kamilaris, A.; Prenafeta-Boldú, F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 2018 , 147 , 70–90. [ Google Scholar ] [ CrossRef ]
• Li, R.; Wang, R.; Zhang, J.; Xie, C.; Liu, L.; Wang, F.; Chen, H.; Chen, T.; Hu, H.; Jia, X.; et al. An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field. IEEE Access 2019 , 7 , 160274–160283. [ Google Scholar ] [ CrossRef ]
• Vélez, S.; Ariza-Sentís, M.; Valente, J. Mapping the spatial variability of Botrytis bunch rot risk in vineyards using UAV multispectral imagery. Eur. J. Agron. 2023 , 142 , 126691. [ Google Scholar ] [ CrossRef ]
• Gomez Selvaraj, M.; Vergara, A.; Montenegro, F.; Alonso Ruiz, H.; Safari, N.; Raymaekers, D.; Ocimati, W.; Ntamwira, J.; Tits, L.; Omondi, A.B.; et al. Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS J. Photogramm. Remote Sens. 2020 , 169 , 110–124. [ Google Scholar ] [ CrossRef ]
• Alshammari, H.H.; Alzahrani, A. Employing a hybrid lion-firefly algorithm for recognition and classification of olive leaf disease in Saudi Arabia. Alexandria. Eng. J. 2023 , 84 , 215–226. [ Google Scholar ] [ CrossRef ]
• Zhang, T.; Xu, Z.; Su, J.; Yang, Z.; Liu, C.; Chen, W.-H.; Li, J. Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery. Remote Sens. 2021 , 13 , 3892. [ Google Scholar ] [ CrossRef ]
• Jin, X.; Jie, L.; Wang, S.; Qi, H.J.; Li, S.W. Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field. Remote Sens. 2018 , 10 , 395. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Lv, C. TinySegformer: A lightweight visual segmentation model for real-time agricultural pest detection. Comput. Electron. Agric. 2024 , 218 , 108740. [ Google Scholar ] [ CrossRef ]
• Lu, S.; Ye, S.-j. Using an image segmentation and support vector machine method for identifying two locust species and instars. J. Integr. Agric. 2020 , 19 , 1301–1313. [ Google Scholar ] [ CrossRef ]
• Barbedo, J.G.A.; Tibola, C.S.; Fernandes, J.M.C. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosyst. Eng. 2015 , 131 , 65–76. [ Google Scholar ] [ CrossRef ]
• Mumtaz, R.; Maqsood, M.H.; Haq Iu Shafi, U.; Mahmood, Z.; Mumtaz, M. Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading. Decis. Anal. J. 2023 , 8 , 100305. [ Google Scholar ] [ CrossRef ]
• Bao, W.; Zhu, Z.; Hu, G.; Zhou, X.; Zhang, D.; Yang, X. UAV remote sensing detection of tea leaf blight based on DDMA-YOLO. Comput. Electron. Agric. 2023 , 205 , 107637. [ Google Scholar ] [ CrossRef ]
• Li, D.; Song, Z.; Quan, C.; Xu, X.; Liu, C. Recent advances in image fusion technology in agriculture. Comput. Electron. Agric. 2021 , 191 , 106491. [ Google Scholar ] [ CrossRef ]
• Ali, M.A.; Sharma, A.K.; Dhanaraj, R.K. Heterogeneous features and deep learning networks fusion-based pest detection, prevention and controlling system using IoT and pest sound analytics in a vast agriculture system. Comput. Electr. Eng. 2024 , 116 , 109146. [ Google Scholar ] [ CrossRef ]
• Lin, Q.; Huang, H.; Wang, J.; Chen, L.; Du, H.; Zhou, G. Early detection of pine shoot beetle attack using vertical profile of plant traits through UAV-based hyperspectral, thermal, and lidar data fusion. Int. J. Appl. Earth Obs. Geoinf. 2023 , 125 , 103549. [ Google Scholar ] [ CrossRef ]
• Dalagnol, R.; Phillips, O.L.; Gloor, E.; Galvão, L.S.; Wagner, F.H.; Locks, C.J.; Aragão, L.E.O.C. Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR. Remote Sens. 2019 , 11 , 817. [ Google Scholar ] [ CrossRef ]
• Pantazi, X.E.; Moshou, D.; Bochtis, D. Chapter 5-Tutorial II: Disease detection with fusion techniques. In Intelligent Data Mining and Fusion Systems in Agriculture ; Pantazi, X.E., Moshou, D., Bochtis, D., Eds.; Academic Press: Cambridge, MA, USA, 2020; pp. 199–221. [ Google Scholar ]
• Kaya, Y.; Gürsoy, E. A novel multi-head CNN design to identify plant diseases using the fusion of RGB images. Ecol. Inform. 2023 , 75 , 101998. [ Google Scholar ] [ CrossRef ]
• Ma, R.; Zhang, N.; Zhang, X.; Bai, T.; Yuan, X.; Bao, H.; He, D.; Sun, W.; He, Y. Cotton Verticillium wilt monitoring based on UAV multispectral-visible multi-source feature fusion. Comput. Electron. Agric. 2024 , 217 , 108628. [ Google Scholar ] [ CrossRef ]
• De Cesaro Júnior, T.; Rieder, R.; Di Domênico, J.R.; Lau, D. InsectCV: A system for insect detection in the lab from trap images. Ecol. Inform. 2022 , 67 , 101516. [ Google Scholar ] [ CrossRef ]
• Ishengoma, F.S.; Rai, I.A.; Ngoga, S.R. Hybrid convolution neural network model for a quicker detection of infested maize plants with fall armyworms using UAV-based images. Ecol. Inform. 2022 , 67 , 101502. [ Google Scholar ] [ CrossRef ]
• Waheed, A.; Goyal, M.; Gupta, D.; Khanna, A.; Hassanien, A.E.; Pandey, H.M. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput. Electron. Agric. 2020 , 175 , 105456. [ Google Scholar ] [ CrossRef ]
• Sunil, C.K.; Jaidhar, C.D.; Patil, N. Tomato plant disease classification using Multilevel Feature Fusion with adaptive channel spatial and pixel attention mechanism. Expert Syst. Appl. 2023 , 228 , 120381. [ Google Scholar ] [ CrossRef ]
• Dong, S.; Teng, Y.; Jiao, L.; Du, J.; Liu, K.; Wang, R. ESA-Net: An efficient scale-aware network for small crop pest detection. Expert Syst. Appl. 2024 , 236 , 121308. [ Google Scholar ] [ CrossRef ]
• Amarathunga, D.C.; Ratnayake, M.N.; Grundy, J.; Dorin, A. Fine-grained image classification of microscopic insect pest species: Western Flower thrips and Plague thrips. Comput. Electron. Agric. 2022 , 203 , 107462. [ Google Scholar ] [ CrossRef ]
• Ye, W.; Lao, J.; Liu, Y.; Chang, C.-C.; Zhang, Z.; Li, H.; Zhou, H. Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model. Ecol. Inform. 2022 , 72 , 101906. [ Google Scholar ] [ CrossRef ]
• Kaliraj, S.; Adhikari, K.; Dharumarajan, S.; Lalitha, M.; Kumar, N. Chapter 3-Remote sensing and geographic information system applications. In Mapping and Assessment of Soil Resources ; Dharumarajan, S., Kaliraj, S., Adhikari, K., Lalitha, M., Kumar, N., Eds.; Remote Sensing of Soils Elsevier: Amsterdam, The Netherlands, 2024; pp. 25–41. [ Google Scholar ]
• Yang, H.; Zhang, X.; Xu, M.; Shao, S.; Wang, X.; Liu, W.; Wu, D.; Ma, Y.; Bao, Y.; Zhang, X.; et al. Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China. Catena 2020 , 184 , 104259. [ Google Scholar ] [ CrossRef ]
• Das, B.; Rathore, P.; Roy, D.; Chakraborty, D.; Bhattacharya, B.K.; Mandal, D.; Jatav, R.; Sethi, D.; Mukherjee, J.; Sehgal, V.K.; et al. Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations. Agric. For. Meteorol. 2023 , 339 , 109567. [ Google Scholar ] [ CrossRef ]
• Dash, P.K. Chapter 22—Remote sensing as a potential tool for advancing digital soil mapping. In Remote Sensing of Soils ; Dharumarajan, S., Kaliraj, S., Adhikari, K., Lalitha, M., Kumar, N., Eds.; Elsevier: Amsterdam, The Netherlands, 2024; pp. 357–370. [ Google Scholar ]
• Das, S.; Ghimire, D. Chapter 25—Soil organic carbon: Measurement and monitoring using remote sensing data. In Remote Sensing of Soils ; Dharumarajan, S., Kaliraj, S., Adhikari, K., Lalitha, M., Kumar, N., Eds.; Elsevier: Amsterdam, The Netherlands, 2024; pp. 395–409. [ Google Scholar ]
• Hareesh, S.B. Chapter 7—The latest applications of remote sensing technologies for soil management in precision agriculture practices. In Remote Sensing in Precision Agriculture ; Lamine, S., Srivastava, P.K., Kayad, A., Muñoz-Arriola, F., Pandey, P.C., Eds.; Academic Press: Cambridge, MA, USA, 2024; pp. 105–135. [ Google Scholar ]
• Peña-Arancibia, J.L.; Mainuddin, M.; Kirby, J.M.; Chiew, F.H.S.; McVicar, T.R.; Vaze, J. Assessing irrigated agriculture’s surface water and groundwater consumption by combining satellite remote sensing and hydrologic modelling. Sci. Total Environ. 2016 , 542 , 372–382. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, Q.; Hao, H.; Zhao, Y.; Geng, Q.; Liu, G.; Zhang, Y.; Yu, F. GANs-LSTM Model for Soil Temperature Estimation From Meteorological: A New Approach. IEEE Access 2020 , 8 , 59427–59443. [ Google Scholar ] [ CrossRef ]
• Li, Q.; Li, Z.; Shangguan, W.; Wang, X.; Li, L.; Yu, F. Improving soil moisture prediction using a novel encoder-decoder model with residual learning. Comput. Electron. Agric. 2022 , 195 , 106816. [ Google Scholar ] [ CrossRef ]
• Mohanty, B.P.; Cosh, M.H.; Lakshmi, V.; Montzka, C. Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zone J. 2017 , 16 , 1–9. [ Google Scholar ] [ CrossRef ]
• Maynard, J.J.; Levi, M.R. Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability. Geoderma 2017 , 285 , 94–109. [ Google Scholar ] [ CrossRef ]
• Duan, M.; Song, X.; Li, Z.; Zhang, X.; Ding, X.; Cui, D. Identifying soil groups and selecting a high-accuracy classification method based on multi-textural features with optimal window sizes using remote sensing images. Ecol. Inform. 2024 , 81 , 102563. [ Google Scholar ] [ CrossRef ]
• Zhou, Q.B.; Yu, Q.Y.; Liu, J.; Wu, W.B.; Tang, H.J. Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring. J. Integr. Agric. 2017 , 16 , 242–251. [ Google Scholar ] [ CrossRef ]
• Musasa, T.; Dube, T.; Marambanyika, T. Landsat satellite programme potential for soil erosion assessment and monitoring in arid environments: A review of applications and challenges. Int. Soil Water Conserv. Res. 2023 , 12 , 267–278. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Zhang, Y.; Song, P.; Tian, J. Estimating sub-daily resolution soil moisture using Fengyun satellite data and machine learning. J. Hydrol. 2024 , 632 , 130814. [ Google Scholar ] [ CrossRef ]
• Kolassa, J.; Reichle, R.H.; Liu, Q.; Alemohammad, S.H.; Gentine, P.; Aida, K.; Asanuma, J.; Bircher, S.; Caldwell, T.; Colliander, A.; et al. Estimating surface soil moisture from SMAP observations using a Neural Network technique. Remote Sens. Environ. 2018 , 204 , 43–59. [ Google Scholar ] [ CrossRef ]
• Wang La Zhou, X.; Zhu, X.; Dong, Z.; Guo, W. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. Crop J. 2016 , 4 , 212–219. [ Google Scholar ] [ CrossRef ]
• Yang, H.; Xiong, L.; Liu, D.; Cheng, L.; Chen, J. High spatial resolution simulation of profile soil moisture by assimilating multi-source remote-sensed information into a distributed hydrological model. J. Hydrol. 2021 , 597 , 126311. [ Google Scholar ] [ CrossRef ]
• Mammadov, E.; Nowosad, J.; Glaesser, C. Estimation and mapping of surface soil properties in the Caucasus Mountains, Azerbaijan using high-resolution remote sensing data. Geoderma Reg. 2021 , 26 , e00411. [ Google Scholar ] [ CrossRef ]
• Straffelini, E.; Pijl, A.; Otto, S.; Marchesini, E.; Pitacco, A.; Tarolli, P. A high-resolution physical modelling approach to assess runoff and soil erosion in vineyards under different soil managements. Soil Tillage Res. 2022 , 222 , 105418. [ Google Scholar ] [ CrossRef ]
• Koley, S.; Jeganathan, C. Estimation and evaluation of high spatial resolution surface soil moisture using multi-sensor multi-resolution approach. Geoderma 2020 , 378 , 114618. [ Google Scholar ] [ CrossRef ]
• Bertalan, L.; Holb, I.; Pataki, A.; Négyesi, G.; Szabó, G.; Kupásné Szalóki, A.; Szabo, S. UAV-based multispectral and thermal cameras to predict soil water content–A machine learning approach. Comput. Electron. Agric. 2022 , 200 , 107262. [ Google Scholar ] [ CrossRef ]
• Menzies Pluer, E.G.; Robinson, D.T.; Meinen, B.U.; Macrae, M.L. Pairing soil sampling with very-high resolution UAV imagery: An examination of drivers of soil and nutrient movement and agricultural productivity in southern Ontario. Geoderma 2020 , 379 , 114630. [ Google Scholar ] [ CrossRef ]
• Cheng, M.; Jiao, X.; Liu, Y.; Shao, M.; Yu, X.; Bai, Y.; Wang, Z.; Wang, S.; Tuohuti, N.; Liu, S.; et al. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning. Agric. Water Manag. 2022 , 264 , 107530. [ Google Scholar ] [ CrossRef ]
• Huuskonen, J.; Oksanen, T. Soil sampling with drones and augmented reality in precision agriculture. Comput. Electron. Agric. 2018 , 154 , 25–35. [ Google Scholar ] [ CrossRef ]
• Shokati, H.; Mashal, M.; Noroozi, A.; Mirzaei, S.; Mohammadi-Doqozloo, Z. Assessing soil moisture levels using visible UAV imagery and machine learning models. Remote Sens. Appl. Soc. Environ. 2023 , 32 , 101076. [ Google Scholar ] [ CrossRef ]
• Wang, Z.; Zhang, X.; Zhang, F.; Chan, N.W.; Kung, H.-t.; Liu, S.; Deng, L. Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China. Ecol. Indic. 2020 , 119 , 106869. [ Google Scholar ] [ CrossRef ]
• Ma, S.; He, B.; Ge, X.; Luo, X. Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images. Ecol. Inform. 2023 , 75 , 102111. [ Google Scholar ] [ CrossRef ]
• Du, R.; Chen, J.; Xiang, Y.; Xiang, R.; Yang, X.; Wang, T.; He, Y.; Wu, Y.; Yin, H.; Zhang, Z.; et al. Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models. Int. Soil Water Conserv. Res. 2023 , 12 , 726–740. [ Google Scholar ] [ CrossRef ]
• Golestani, M.; Mosleh Ghahfarokhi, Z.; Esfandiarpour-Boroujeni, I.; Shirani, H. Evaluating the spatiotemporal variations of soil salinity in Sirjan Playa, Iran using Sentinel-2A and Landsat-8 OLI imagery. Catena 2023 , 231 , 107375. [ Google Scholar ] [ CrossRef ]
• Sothe, C.; Gonsamo, A.; Arabian, J.; Snider, J. Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations. Geoderma 2022 , 405 , 115402. [ Google Scholar ] [ CrossRef ]
• Rahman, A.; Abdullah, H.M.; Tanzir, M.T.; Hossain, M.J.; Khan, B.M.; Miah, M.G.; Islam, I. Performance of different machine learning algorithms on satellite image classification in rural and urban setup. Remote Sens. Appl. Soc. Environ. 2020 , 20 , 100410. [ Google Scholar ] [ CrossRef ]
• Huang, H.; Wang, J.; Liu, C.; Liang, L.; Li, C.; Gong, P. The migration of training samples towards dynamic global land cover mapping. ISPRS J. Photogramm. Remote Sens. 2020 , 161 , 27–36. [ Google Scholar ] [ CrossRef ]
• Zafar, Z.; Zubair, M.; Zha, Y.; Fahd, S.; Ahmad Nadeem, A. Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. Egypt. J. Remote Sens. Space Sci. 2024 , 27 , 216–226. [ Google Scholar ] [ CrossRef ]
• Elhadi, M.I.A.; Mutanga, O.; Odindi, J.; Abdel-Rahman, E.M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 2014 , 35 , 3440–3458. [ Google Scholar ]
• Gómez, C.; White, J.C.; Wulder, M.A. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogramm. Remote Sens. 2016 , 116 , 55–72. [ Google Scholar ] [ CrossRef ]
• Matlhodi, B.; Kenabatho, P.K.; Parida, B.P.; Maphanyane, J.G. Evaluating Land Use and Land Cover Change in the Gaborone Dam Catchment, Botswana, from 1984–2015 Using GIS and Remote Sensing. Sustainability 2019 , 11 , 5174. [ Google Scholar ] [ CrossRef ]
• Liu, J.; Yang, K.; Tariq, A.; Lu, L.; Soufan, W.; El Sabagh, A. Interaction of climate, topography and soil properties with cropland and cropping pattern using remote sensing data and machine learning methods. Egypt. J. Remote Sens. Space Sci. 2023 , 26 , 415–426. [ Google Scholar ] [ CrossRef ]
• Yuh, Y.G.; Tracz, W.; Matthews, H.D.; Turner, S.E. Application of machine learning approaches for land cover monitoring in northern Cameroon. Ecol. Inform. 2023 , 74 , 101955. [ Google Scholar ] [ CrossRef ]
• Khatami, R.; Mountrakis, G.; Stehman, S.V. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sens. Environ. 2016 , 177 , 89–100. [ Google Scholar ] [ CrossRef ]
• Nitze, I.; Barrett, B.; Cawkwell, F. Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series. Int. J. Appl. Earth Obs. Geoinf. 2015 , 34 , 136–146. [ Google Scholar ] [ CrossRef ]
• Zhang, S.; Liu, L.Y. The potential of the MERIS Terrestrial Chlorophyll Index for crop yield prediction. Remote Sens. Lett. 2014 , 5 , 733–742. [ Google Scholar ] [ CrossRef ]
• Teodoro, A. Applicability of data mining algorithms in the identification of beach features/patterns on high-resolution satellite data. J. Appl. Remote Sens. 2015 , 9 , 095095. [ Google Scholar ] [ CrossRef ]
• Sinha, S.; Sharma, L.K.; Nathawat, M.S. Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. Egypt. J. Remote Sens. Space Sci. 2015 , 18 , 217–233. [ Google Scholar ] [ CrossRef ]
• Mei, A.; Manzo, C.; Fontinovo, G.; Bassani, C.; Allegrini, A.; Petracchini, F. Assessment of land cover changes in Lampedusa Island (Italy) using Landsat TM and OLI data. J. Afr. Earth Sci. 2016 , 122 , 15–24. [ Google Scholar ] [ CrossRef ]
• Silva, L.P.E.; Xavier, A.P.C.; da Silva, R.M.; Santos, C.A.G. Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil. Glob. Ecol. Conserv. 2020 , 21 , e00811. [ Google Scholar ] [ CrossRef ]
• Zhang, H.K.; Roy, D.P.; Luo, D. Demonstration of large area land cover classification with a one dimensional convolutional neural network applied to single pixel temporal metric percentiles. Remote Sens. Environ. 2023 , 295 , 113653. [ Google Scholar ] [ CrossRef ]
• Zhang, C.; Yue, P.; Tapete, D.; Shangguan, B.; Wang, M.; Wu, Z. A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images. Int. J. Appl. Earth Obs. Geoinf. 2020 , 88 , 102086. [ Google Scholar ] [ CrossRef ]
• Loukika, K.N.; Keesara, V.R.; Sridhar, V. Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability 2021 , 13 , 13758. [ Google Scholar ] [ CrossRef ]
• Prasad, P.; Loveson, V.J.; Chandra, P.; Kotha, M. Evaluation and comparison of the earth observing sensors in land cover/land use studies using machine learning algorithms. Ecol. Inform. 2022 , 68 , 101522. [ Google Scholar ] [ CrossRef ]
• Zhou, X.; Zheng, H.B.; Xu, X.Q.; He, J.Y.; Ge, X.K.; Yao, X.; Cheng, T.; Zhu, Y.; Cao, W.X.; Tian, Y.C. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS J. Photogramm. Remote Sens. 2017 , 130 , 246–255. [ Google Scholar ] [ CrossRef ]
• Wang, L.; Tian, Y.; Yao, X.; Zhu, Y.; Cao, W. Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images. Field Crops Res. 2014 , 164 , 178–188. [ Google Scholar ] [ CrossRef ]
• Furukawa, F.; Maruyama, K.; Saito, Y.K.; Kaneko, M. Corn Height Estimation Using UAV for Yield Prediction and Crop Monitoring. In Unmanned Aerial Vehicle: Applications in Agriculture and Environment ; Avtar, R., Watanabe, T., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 51–69. [ Google Scholar ]
• Johnson, D.M. An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 2014 , 141 , 116–128. [ Google Scholar ] [ CrossRef ]
• Shao, M.; Nie, C.; Zhang, A.; Shi, L.; Zha, Y.; Xu, H.; Yang, H.; Yu, X.; Bai, Y.; Liu, S.; et al. Quantifying effect of maize tassels on LAI estimation based on multispectral imagery and machine learning methods. Comput. Electron. Agric. 2023 , 211 , 108029. [ Google Scholar ] [ CrossRef ]
• Yang, C.; Lee, W.S.; Gader, P. Hyperspectral band selection for detecting different blueberry fruit maturity stages. Comput. Electron. Agric. 2014 , 109 , 23–31. [ Google Scholar ] [ CrossRef ]
• Peña, M.A.; Brenning, A. Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile. Remote Sens. Environ. 2015 , 171 , 234–244. [ Google Scholar ] [ CrossRef ]
• Liang, L.; Di, L.; Zhang, L.; Deng, M.; Qin, Z.; Zhao, S.; Lin, H. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sens. Environ. 2015 , 165 , 123–134. [ Google Scholar ] [ CrossRef ]
• Yang, Z.; Shao, Y.; Li, K.; Liu, Q.; Liu, L.; Brisco, B. An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data. Remote Sens. Environ. 2017 , 195 , 184–201. [ Google Scholar ] [ CrossRef ]
• Azadbakht, M.; Ashourloo, D.; Aghighi, H.; Homayouni, S.; Shahrabi, H.S.; Matkan, A.; Radiom, S. Alfalfa yield estimation based on time series of Landsat 8 and PROBA-V images: An investigation of machine learning techniques and spectral-temporal features. Remote Sens. Appl. Soc. Environ. 2022 , 25 , 100657. [ Google Scholar ] [ CrossRef ]
• Görgens, E.B.; Montaghi, A.; Rodriguez, L.C.E. A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics. Comput. Electron. Agric. 2015 , 116 , 221–227. [ Google Scholar ] [ CrossRef ]
• Guo, Z.; Chamberlin, J.; You, L. Smallholder maize yield estimation using satellite data and machine learning in Ethiopia. Crop Environ. 2023 , 2 , 165–174. [ Google Scholar ] [ CrossRef ]
• Van Ewijk, K.Y.; Randin, C.F.; Treitz, P.M.; Scott, N.A. Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery. Remote Sens. Environ. 2014 , 150 , 120–131. [ Google Scholar ] [ CrossRef ]
• Khanal, S.; Klopfenstein, A.; Kc, K.; Ramarao, V.; Fulton, J.; Douridas, N.; Shearer, S.A. Assessing the impact of agricultural field traffic on corn grain yield using remote sensing and machine learning. Soil Tillage Res. 2021 , 208 , 104880. [ Google Scholar ] [ CrossRef ]
• Habibi, L.N.; Matsui, T.; Tanaka, T.S.T. Critical evaluation of the effects of a cross-validation strategy and machine learning optimization on the prediction accuracy and transferability of a soybean yield prediction model using UAV-based remote sensing. J. Agric. Food Res. 2024 , 16 , 101096. [ Google Scholar ] [ CrossRef ]
• Zhang, S.; Qi, X.; Gao, M.; Dai, C.; Yin, G.; Ma, D.; Feng, W.; Guo, T.; He, L. Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms. Food Chem. 2024 , 448 , 139103. [ Google Scholar ] [ CrossRef ]
• Guo, Y.; Xiao, Y.; Hao, F.; Zhang, X.; Chen, J.; de Beurs, K.; He, Y.; Fu, Y.H. Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images. Int. J. Appl. Earth Obs. Geoinf. 2023 , 124 , 103528. [ Google Scholar ] [ CrossRef ]
• Qu, H.; Zheng, C.; Ji, H.; Barai, K.; Zhang, Y.-J. A fast and efficient approach to estimate wild blueberry yield using machine learning with drone photography: Flight altitude, sampling method and model effects. Comput. Electron. Agric. 2024 , 216 , 108543. [ Google Scholar ] [ CrossRef ]
• Yu, N.; Li, L.; Schmitz, N.; Tian, L.F.; Greenberg, J.A.; Diers, B.W. Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sens. Environ. 2016 , 187 , 91–101. [ Google Scholar ] [ CrossRef ]
• Maimaitijiang, M.; Ghulam, A.; Sidike, P.; Hartling, S.; Maimaitiyiming, M.; Peterson, K.; Shavers, E.; Fishman, J.; Peterson, J.; Kadam, S.; et al. Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS J. Photogramm. Remote Sens. 2017 , 134 , 43–58. [ Google Scholar ] [ CrossRef ]
• Xu, W.; Chen, P.; Zhan, Y.; Chen, S.; Zhang, L.; Lan, Y. Cotton yield estimation model based on machine learning using time series UAV remote sensing data. Int. J. Appl. Earth Obs. Geoinf. 2021 , 104 , 102511. [ Google Scholar ] [ CrossRef ]
• Liu, S.; Jin, X.; Bai, Y.; Wu, W.; Cui, N.; Cheng, M.; Liu, Y.; Meng, L.; Jia, X.; Nie, C.; et al. UAV multispectral images for accurate estimation of the maize LAI considering the effect of soil background. Int. J. Appl. Earth Obs. Geoinf. 2023 , 121 , 103383. [ Google Scholar ] [ CrossRef ]
• Kern, A.; Barcza, Z.; Marjanović, H.; Árendás, T.; Fodor, N.; Bónis, P.; Bognár, P.; Lichtenberger, J. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agric. For. Meteorol. 2018 , 260 , 300–320. [ Google Scholar ] [ CrossRef ]
• Bai, H.; Xiao, D.; Tang, J.; Liu, D.L. Evaluation of wheat yield in North China Plain under extreme climate by coupling crop model with machine learning. Comput. Electron. Agric. 2024 , 217 , 108651. [ Google Scholar ] [ CrossRef ]
• Khanal, S.; Fulton, J.; Klopfenstein, A.; Douridas, N.; Shearer, S. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput. Electron. Agric. 2018 , 153 , 213–225. [ Google Scholar ] [ CrossRef ]
• Jagdeep, S.; Gobinder, S.; Gupta, N. Balancing phosphorus fertilization for sustainable maize yield and soil test phosphorus management: A long-term study using machine learning. Field Crops Res. 2023 , 304 , 109169. [ Google Scholar ] [ CrossRef ]
• Fry, J.; Guber, A.K.; Ladoni, M.; Munoz, J.D.; Kravchenko, A.N. The effect of up-scaling soil properties and model parameters on predictive accuracy of DSSAT crop simulation model under variable weather conditions. Geoderma 2017 , 287 , 105–115. [ Google Scholar ] [ CrossRef ]
• Zain, M.; Si, Z.; Li, S.; Gao, Y.; Mehmood, F.; Rahman, S.-U.; Mounkaila Hamani, A.K.; Duan, A. The Coupled Effects of Irrigation Scheduling and Nitrogen Fertilization Mode on Growth, Yield and Water Use Efficiency in Drip-Irrigated Winter Wheat. Sustainability 2021 , 13 , 2742. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Shi, W.; Wen, T. Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application. Agric. Water Manag. 2023 , 277 , 108140. [ Google Scholar ] [ CrossRef ]
• Kaur Dhaliwal, J.; Panday, D.; Saha, D.; Lee, J.; Jagadamma, S.; Schaeffer, S.; Mengistu, A. Predicting and interpreting cotton yield and its determinants under long-term conservation management practices using machine learning. Comput. Electron. Agric. 2022 , 199 , 107107. [ Google Scholar ] [ CrossRef ]
• Elavarasan, D.; Vincent, D.R.; Sharma, V.; Zomaya, A.Y.; Srinivasan, K. Forecasting yield by integrating agrarian factors and machine learning models: A survey. Comput. Electron. Agric. 2018 , 155 , 257–282. [ Google Scholar ] [ CrossRef ]
• Singh, B.; Jana, A.K. Forecast of agri-residues generation from rice, wheat and oilseed crops in India using machine learning techniques: Exploring strategies for sustainable smart management. Environ. Res. 2024 , 245 , 117993. [ Google Scholar ] [ CrossRef ]
• Zhou, H.K.; Yang, J.H.; Lou, W.D.; Sheng, L.; Li, D.; Hu, H. Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery. Front. Plant Sci. 2023 , 14 , 1217448. [ Google Scholar ] [ CrossRef ]
• Habyarimana, E.; Piccard, I.; Catellani, M.; De Franceschi, P.; Dall’Agata, M. Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques. Agronomy 2019 , 9 , 203. [ Google Scholar ] [ CrossRef ]
• Kowalik, W.; Dabrowska-Zielinska, K.; Meroni, M.; Raczka, T.U.; de Wit, A. Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries. Int. J. Appl. Earth Obs. Geoinf. 2014 , 32 , 228–239. [ Google Scholar ] [ CrossRef ]
• Castaldi, F.; Casa, R.; Pelosi, F.; Yang, H. Influence of acquisition time and resolution on wheat yield estimation at the field scale from canopy biophysical variables retrieved from SPOT satellite data. Int. J. Remote Sens. 2015 , 36 , 2438–2459. [ Google Scholar ] [ CrossRef ]
• Naghdyzadegan Jahromi, M.; Zand-Parsa, S.; Razzaghi, F.; Jamshidi, S.; Didari, S.; Doosthosseini, A.; Pourghasemi, H.R. Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices. Eur. J. Agron. 2023 , 146 , 126820. [ Google Scholar ] [ CrossRef ]
• Jurečka, F.; Fischer, M.; Hlavinka, P.; Balek, J.; Semerádová, D.; Bláhová, M.; Anderson, M.C.; Hain, C.; Žalud, Z.; Trnka, M. Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic. Agric. Water Manag. 2021 , 256 , 107064. [ Google Scholar ] [ CrossRef ]
• Yang, C.; Lei, H. Evaluation of data assimilation strategies on improving the performance of crop modeling based on a novel evapotranspiration assimilation framework. Agric. For. Meteorol. 2024 , 346 , 109882. [ Google Scholar ] [ CrossRef ]
• Gilardelli, C.; Stella, T.; Confalonieri, R.; Ranghetti, L.; Campos-Taberner, M.; García-Haro, F.J.; Boschetti, M. Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data. Eur. J. Agron. 2019 , 103 , 108–116. [ Google Scholar ] [ CrossRef ]
• Gaso, D.V.; de Wit, A.; Berger, A.G.; Kooistra, L. Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model. Agric. For. Meteorol. 2021 , 308 , 108553. [ Google Scholar ] [ CrossRef ]
• Liu, C.; Liu, Y.; Lu, Y.H.; Liao, Y.L.; Nie, J.; Yuan, X.L.; Chen, F. Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity. PeerJ 2019 , 6 . [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Singh, V.; Kunal Singh, M.; Singh, B. Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice. Pedosphere 2022 , 32 , 812–822. [ Google Scholar ] [ CrossRef ]
• Zhang, J.; Feng, L.; Yao, F. Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information. ISPRS J. Photogramm. Remote Sens. 2014 , 94 , 102–113. [ Google Scholar ] [ CrossRef ]
• De la Casa, A.; Ovando, G.; Bressanini, L.; Martínez, J.; Díaz, G.; Miranda, C. Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot. ISPRS J. Photogramm. Remote Sens. 2018 , 146 , 531–547. [ Google Scholar ] [ CrossRef ]
• Kitano, B.T.; Mendes, C.C.T.; Geus, A.R.; Oliveira, H.C.; Souza, J.R. Corn Plant Counting Using Deep Learning and UAV Images. IEEE Geosci. Remote Sens. Lett. 2019 , 1–5. [ Google Scholar ] [ CrossRef ]
• Jhajharia, K.; Mathur, P. Prediction of crop yield using satellite vegetation indices combined with machine learning approaches. Adv. Space Res. 2023 , 72 , 3998–4007. [ Google Scholar ] [ CrossRef ]
• Shammi, S.A.; Meng, Q. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecol. Indic. 2021 , 121 , 107124. [ Google Scholar ] [ CrossRef ]
• Zhao, Y.; Vergopolan, N.; Baylis, K.; Blekking, J.; Caylor, K.; Evans, T.; Giroux, S.; Sheffield, J.; Estes, L. Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem. Agric. For. Meteorol. 2018 , 262 , 147–156. [ Google Scholar ] [ CrossRef ]
• Zhang, H.; Wang, L.; Tian, T.; Yin, J. A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China. Remote Sens. 2021 , 13 , 1221. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.X.; Walker, J.P.; Pauwels, V.R.N.; Sadeh, Y. Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study. Remote Sens. 2022 , 14 , 65. [ Google Scholar ] [ CrossRef ]
• Kheir, A.M.S.; Mkuhlani, S.; Mugo, J.W.; Elnashar, A.; Nangia, V.; Devare, M.; Govind, A. Integrating APSIM model with machine learning to predict wheat yield spatial distribution. Agron. J. 2023 , 115 , 3188–3196. [ Google Scholar ] [ CrossRef ]
• Bai, T.; Zhang, N.; Mercatoris, B.; Chen, Y. Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model. Remote Sens. 2019 , 11 , 1119. [ Google Scholar ] [ CrossRef ]
• Tie-cheng, B.; Wang, T.; Zhang, N.N.; Chen, Y.Q.; Mercatoris, B. Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model. J. Integr. Agric. 2020 , 19 , 721–734. [ Google Scholar ] [ CrossRef ]
• Shi, Y.; Wang, Z.; Hou, C.; Zhang, P. Yield estimation of Lycium barbarum L. based on the WOFOST model. Ecol. Model. 2022 , 473 , 110146. [ Google Scholar ] [ CrossRef ]
• Bellakanji, A.C.; Zribi, M.; Lili-Chabaane, Z.; Mougenot, B. Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images. Sensors 2018 , 18 , 2138. [ Google Scholar ] [ CrossRef ]
• Huang, J.; Sedano, F.; Huang, Y.; Ma, H.; Li, X.; Liang, S.; Tian, L.; Zhang, X.; Fan, J.; Wu, W. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 2016 , 216 , 188–202. [ Google Scholar ] [ CrossRef ]
• Fattori Junior, I.M.; dos Santos Vianna, M.; Marin, F.R. Assimilating leaf area index data into a sugarcane process-based crop model for improving yield estimation. Eur. J. Agron. 2022 , 136 , 126501. [ Google Scholar ] [ CrossRef ]
• Hu, S.; Shi, L.; Huang, K.; Zha, Y.; Hu, X.; Ye, H.; Yang, Q. Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation. Field Crops Res. 2019 , 232 , 49–61. [ Google Scholar ] [ CrossRef ]
• Tang, Y.; Zhou, R.; He, P.; Yu, M.; Zheng, H.; Yao, X.; Cheng, T.; Zhu, Y.; Cao, W.; Tian, Y. Estimating wheat grain yield by assimilating phenology and LAI with the WheatGrow model based on theoretical uncertainty of remotely sensed observation. Agric. For. Meteorol. 2023 , 339 , 109574. [ Google Scholar ] [ CrossRef ]
• Li, Z.; Ding, L.; Shen, B.; Chen, J.; Xu, D.; Wang, X.; Fang, W.; Pulatov, A.; Kussainova, M.; Amarjargal, A.; et al. Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model. Sci. Total Environ. 2024 , 909 , 168594. [ Google Scholar ] [ CrossRef ]
• Xue, H.; Xu, X.; Zhu, Q.; Meng, Y.; Long, H.; Li, H.; Song, X.; Yang, G.; Yang, M.; Li, Y.; et al. Rice yield and quality estimation coupling hierarchical linear model with remote sensing. Comput. Electron. Agric. 2024 , 218 , 108731. [ Google Scholar ] [ CrossRef ]
• Pandey, D.K.; Mishra, R. Towards sustainable agriculture: Harnessing AI for global food security. Artif. Intell. Agric. 2024 , 12 , 72–84. [ Google Scholar ] [ CrossRef ]
• Liu, Q.; Wang, C.; Jiang, J.; Wu, J.; Wang, X.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; Liu, X. Multi-source data fusion improved the potential of proximal fluorescence sensors in predicting nitrogen nutrition status across winter wheat growth stages. Comput. Electron. Agric. 2024 , 219 , 108786. [ Google Scholar ] [ CrossRef ]
• Zhao, M.; Meng, Q.; Wang, L.; Zhang, L.; Hu, X.; Shi, W. Towards robust classification of multi-view remote sensing images with partial data availability. Remote Sens. Environ. 2024 , 306 , 114112. [ Google Scholar ] [ CrossRef ]
• Baltodano, A.; Agramont, A.; Lekarkar, K.; Spyrakos, E.; Reusen, I.; van Griensven, A. Exploring global remote sensing products for water quality assessment: Lake Nicaragua case study. Remote Sens. Appl. Soc. Environ. 2024 , 36 , 101331. [ Google Scholar ] [ CrossRef ]
• Zhang, H.K.; Qiu, S.; Suh, J.W.; Luo, D.; Zhu, Z. Machine Learning and Deep Learning in Remote Sensing Data Analysis. In Reference Module in Earth Systems and Environmental Sciences ; Elsevier: Amsterdam, The Netherlands, 2024. [ Google Scholar ]
• Feng, H.; Li, Q.; Wang, W.; Bashir, A.K.; Singh, A.K.; Xu, J.; Fang, K. Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework. Inf. Fusion 2024 , 112 , 102555. [ Google Scholar ] [ CrossRef ]
• Joshi, P.; Sandhu, K.S.; Singh Dhillon, G.; Chen, J.; Bohara, K. Detection and monitoring wheat diseases using unmanned aerial vehicles (UAVs). Comput. Electron. Agric. 2024 , 224 , 109158. [ Google Scholar ] [ CrossRef ]
• Wu, Z.; Luo, J.; Rao, K.; Lin, H.; Song, X. Estimation of wheat kernel moisture content based on hyperspectral reflectance and satellite multispectral imagery. Int. J. Appl. Earth Obs. Geoinf. 2024 , 126 , 103597. [ Google Scholar ] [ CrossRef ]
• Qin, P.; Huang, H.; Tang, H.; Wang, J.; Liu, C. MUSTFN: A spatiotemporal fusion method for multi-scale and multi-sensor remote sensing images based on a convolutional neural network. Int. J. Appl. Earth Obs. Geoinf. 2022 , 115 , 103113. [ Google Scholar ] [ CrossRef ]
• Marin, D.B.; Ferraz, G.A.e.S.; Santana, L.S.; Barbosa, B.D.S.; Barata, R.A.P.; Osco, L.P.; Ramos, A.P.M.; Guimarães, P.H.S. Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models. Comput. Electron. Agric. 2021 , 190 , 106476. [ Google Scholar ] [ CrossRef ]
• López-Pérez, E.; Sanchis-Ibor, C.; Jiménez-Bello, M.Á.; Pulido-Velazquez, M. Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing. Agric. Water Manag. 2024 , 302 , 108988. [ Google Scholar ] [ CrossRef ]
• Hao, S.; Ryu, D.; Western, A.W.; Perry, E.; Bogena, H.; Franssen, H.J.H. Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs. Ecol. Model. 2024 , 487 , 110551. [ Google Scholar ] [ CrossRef ]
• Fawakherji, M.; Suriani, V.; Nardi, D.; Bloisi, D.D. Shape and style GAN-based multispectral data augmentation for crop/weed segmentation in precision farming. Crop Prot. 2024 , 184 , 106848. [ Google Scholar ] [ CrossRef ]
• Dos Santos, E.P.; Moreira, M.C.; Fernandes-Filho, E.I.; Demattê, J.A.M.; Santos, U.J.d.; da Silva, D.D.; Cruz, R.R.P.; Moura-Bueno, J.M.; Santos, I.C.; Sampaio, E.V.d.S.B. Improving the generalization error and transparency of regression models to estimate soil organic carbon using soil reflectance data. Ecol. Inform. 2023 , 77 , 102240. [ Google Scholar ] [ CrossRef ]
• Goodridge, W.; Bernard, M.; Jordan, R.; Rampersad, R. Intelligent diagnosis of diseases in plants using a hybrid Multi-Criteria decision making technique. Comput. Electron. Agric. 2017 , 133 , 80–87. [ Google Scholar ] [ CrossRef ]
• Kumar, V.; Sharma, K.V.; Kedam, N.; Patel, A.; Kate, T.R.; Rathnayake, U. A comprehensive review on smart and sustainable agriculture using IoT technologies. Smart Agric. Technol. 2024 , 8 , 100487. [ Google Scholar ] [ CrossRef ]
• Zhou, J.; Gu, X.; Gong, H.; Yang, X.; Sun, Q.; Guo, L.; Pan, Y. Intelligent classification of maize straw types from UAV remote sensing images using DenseNet201 deep transfer learning algorithm. Ecol. Indic. 2024 , 166 , 112331. [ Google Scholar ] [ CrossRef ]
• Prasanna Lakshmi, G.S.; Asha, P.N.; Sandhya, G.; Vivek Sharma, S.; Shilpashree, S.; Subramanya, S.G. An intelligent IOT sensor coupled precision irrigation model for agriculture. Meas. Sens. 2023 , 25 , 100608. [ Google Scholar ] [ CrossRef ]
• Bissadu, K.D.; Sonko, S.; Hossain, G. Society 5.0 enabled agriculture: Drivers, enabling technologies, architectures, opportunities, and challenges. Inf. Process. Agric. 2024 . [ Google Scholar ] [ CrossRef ]
• Et-taibi, B.; Abid, M.R.; Boufounas, E.-M.; Morchid, A.; Bourhnane, S.; Abu Hamed, T.; Benhaddou, D. Enhancing water management in smart agriculture: A cloud and IoT-Based smart irrigation system. Results Eng. 2024 , 22 , 102283. [ Google Scholar ] [ CrossRef ]
• Rostami, K.; Salehi, L. Rural cooperatives social responsibility in promoting Sustainability-oriented Activities in the agricultural sector: Nexus of community, enterprise, and government. Sustain. Futures 2024 , 7 , 100150. [ Google Scholar ] [ CrossRef ]
• Pingali, P.; Plavšić, M. Hunger and environmental goals for Asia: Synergies and trade-offs among the SDGs. Environ. Chall. 2022 , 7 , 100491. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

	Model Name	Application of Precision Agriculture	Reference
Supervised Learning	Naive Bayes	Classification of different crop diseases, soil types, etc.; prediction of the yield of wheat, corn, and other crops.	[ , ]
	Logistic Regression	Assessment of the risk level of pest occurrence; prediction of the yield of wheat, corn, and other crops.	[ , ]
	Linear Regression	Optimization of the amount of fertilizer application to improve the prediction accuracy of wheat, corn, and other crops yield.	[ , ]
	Lasso Regression	Detection of the extent to which crops are attacked by diseases and insect pests.	[ , ]
	AdaBoosT Algorithm	Classification and identification of different crop species and detection of crop diseases and insect pests.	[ , ]
	Linear Discriminant Analysis	Classification of soil types, identification of crop varieties, and determination of the effects of different soil fertilities on crop growth.	[ , ]
	Recurrent Neural Network	Analysis of crop growth time series data and prediction of time series changes in crop diseases and insect pests.	[ , ]
	Decision Tree	Selection of pest management strategies; identification of crop pest types.	[ , ]
	Nearest Neighbor Algorithm	Identification of different crop varieties; evaluation of soil fertility grades.	[ , ]
	XGBoost Algorithm	Prediction of yield of wheat, corn, and other crops based on climate, soil conditions, and other variables.	[ , ]
	Long Short-Term Memory Network	Forecasting the long-term trend of crop yield based on climate variables, such as precipitation and temperature, and prediction of the outbreak of crop diseases and insect pests by time series.	[ , ]
	Support Vector Regression	Crop growth monitoring and modeling, using remote sensing reflectance data to predict crop leaf area index, yield, etc.	[ , ]
	Artificial Neural Network	Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc.	[ , ]
	Convolutional Neural Algorithm	Identification of crop leaf diseases and detection of disease invasion degree of crop leaves; prediction of crop leaf area index, yield, etc.	[ , ]
	Random Forest	Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc.	[ , ]
	Support Vector Machine	Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc.	[ , ]
	CatBoosT Algorithm	Identification of crop leaf diseases and detection of disease invasion degree of crop leaves.	[ , ]
	Ridge Regression	Prediction of soil nutrients and key nutrient content based on soil sample data.	[ , ]
	Random Gradient Descent	Optimization of model parameters to improve the accuracy of agricultural prediction and decision-making models; application to complex agricultural system modeling and prediction.	[ , ]
Semi supervised learning	Generative Semi-Supervised Learning	Assessment of soil quality; prediction of soil fertility, acidity, alkalinity, etc.; prediction and control of diseases and insect pests.	[ , ]
	Autoencoders	Identification and classification of diseases and insect pests; assessment of the risk level of pest occurrence.	[ ]
Unsupervised	Co-Training	Identification, classification, and risk assessment of diseases and insect pests; soil type classification.	[ ]
Learning	Probabilistic Graphical Model	Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc.	[ ]
	Independent Component Analysis	Identification, classification, and risk assessment of diseases and insect pests; soil type classification.	[ ]
	Anomaly Detection Algorithm	Detection of crop wilt, soil moisture, and pH anomaly.	[ ]
	Self-Organizing Maps	Classification of crops and rapid identification of soil types.	[ ]
	K-Means Clustering	Accurate identification of crops.	[ ]
	Principal Component Analysis	Accurate classification of crops based on their growth characteristics (such as color, texture, size, etc.).	[ ]
Reinforcement	Deep Q-Network	Retrieval of key growth information, such as vegetation index, to effectively monitor crop growth and development.	[ ]
	Policy Gradient Methods	Optimization of crop irrigation and fertilization strategies.	[ ]
	Q-learning	Optimization of agricultural decision making and environmental interaction.	[ ]

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Share and Cite

Wang, J.; Wang, Y.; Li, G.; Qi, Z. Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications. Agronomy 2024 , 14 , 1975. https://doi.org/10.3390/agronomy14091975

Wang J, Wang Y, Li G, Qi Z. Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications. Agronomy . 2024; 14(9):1975. https://doi.org/10.3390/agronomy14091975

Wang, Jun, Yanlong Wang, Guang Li, and Zhengyuan Qi. 2024. "Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications" Agronomy 14, no. 9: 1975. https://doi.org/10.3390/agronomy14091975

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International Journal of Research Studies in Agricultural Sciences (IJRSAS

To investigate yield, the competitive and intercropping advantage of carrot-rosemary intercropping over solitary a field study was carried out at Wondo genet Agricultural Research Center during 2016-2017 and 2017-2018 cropping seasons under irrigated condition. The experiment comprised of six treatments: sole carrot, sole Rosemary and four carrot-rosemary intercropping with mix proportion: 100 carrot: 25 rosemary, 100 carrots: 50 rosemary, 100 carrots: 75 rosemary and 100 carrot: 100 rosemary, using randomized complete block design with three replications. Analysis of variance revealed that; intercropping of carrot with different population densities of rosemary significantly affected economic yield; the highest yield was obtained at mono-cropping than that of intercropped. Similarly; essential oil yield of rosemary significantly influenced by cropping system; the highest essential oil yield gained in sole planted than intercropped. The highest value of the land equivalent ratio (1.73), land equivalent coefficient (0.84) and relative crowding coefficient (31.17) obtained when carrot intercropped with rosemary at 100 % population density. However, minimum actual yield loss and maximum intercropping advantage obtained in treatments where carrot intercropped with rosemary at 25 % population density. Generally, these findings suggest that intercropping of carrot with rosemary at 100 % population density enhanced yield advantage and Competitiveness as indicated by higher land equivalent ratio and relative crowding coefficient. Therefore, the inclusion of carrot with 100 % a rosemary population density raised yield advantage and competitiveness over solely planted crop per unit area as indicated by higher LER and relative crowding coefficient.

Related Papers

Ashenafi Nigussie

Different cropping system and planting patterns of onion and rosemary evaluated to estimate yield advantage and their competitiveness during 2013-14 and 2014-15 growing seasons under irrigated condition at wondo genet Sidama zone, Southern Ethiopia. The experiment comprised of six treatments: sole onion (250, 000 plants ha), sole rosemary (83, 333 plants ha) and four onion-rosemary intercropping mix 1 1 proportion: 100 onion: 80 rosemary, 100 onion: 60 rosemary, 100 onion: 40 rosemary and 100 onion: 20 rosemary, using randomized complete block design with three replications. Analysis of variance revealed that; intercropping of onion with different population densities of rosemary significantly affected dry bulb yield; highest dry bulb yield was recorded at solitary cropping than that of intercropped. Similarly; essential oil yield of rosemary significantly influenced by cropping system; highest essential oil yield obtained in sole planted than intercropped. The highest value of land equivalent ratio (1.52), land equivalent coefficient (0.57) and relative crowding coefficient (6.07) obtained when onion intercropped with 80 % rosemary population density. However, positive values of actual yield loss and maximum intercropping advantage obtained in treatments where onion intercropped with rosemary at 20 and 40 % population density. Generally, these finding suggest that intercropping of onion with rosemary at 80 % population density enhanced yield advantage and Competitiveness as indicated by higher land equivalent ratio and relative crowding coefficient. Therefore, the inclusion of onion with 80% a rosemary population density elevated yield advantage and competitiveness over sole planted crop per unit area as indicated by higher land equivalent ratio and relative crowding coefficient.

International Journal of Research Studies in Agricultural Sciences

Nibret Tadesse

Revista Caatinga

Maiele Leandro da silva

The objective of this study was to evaluate the agro-economic efficiency of the intercropping of carrot with cowpea-vegetable under different spatial arrangements and population densities in the semi-arid conditions of the Brazilian Northeast. The study was conducted at the "Rafael…

Spatial arrangement and population density of component cultures, when well structured, may contribute to increased crop yields relative to monocultures. Thus, the objective of this work was to evaluate the productive performance of carrot and cowpea in an intercropping system under different spatial arrangements and population densities. This research was conducted on the “Rafael Fernandes” experimental farm of the Universidade Federal Rural do Semi-Árido (UFERSA), Mossoró, RN, Brazil. The experimental design was a randomized complete block with four replicates, where the treatments were arranged in a 3 × 4 factorial scheme, in which the first factor was three spatial arrangements (2:2, 3:3, and 4:4) and the second factor was four different population densities of cowpea (100%, 80%, 60%, and 40% of the recommended population in the single crop [RPSC]). Rooster tree Calotropis procera (Ait.) R.Br., a spontaneous species of the „Caatinga‟ biome, was used as fertilizer. The characteri...

ABSTRACT: The objective of this study was to evaluate the agronomic efficiency of intercropping combinations of carrot and arugula at different population densities in bicropping in the semi-arid conditions of the Brazilian Northeast. The study was conducted at the "Rafael Fernandes" Experimental Farm of the Universidade Federal Rural do Semi-Árido (UFERSA) during the period September 2011 to February 2012. The experimental design was of randomized complete blocks with treatments arranged in a 4 x 4 factorial scheme with four replications. The combinations were four population densities of carrot (40, 60, 80 and 100% of the recommended population in sole crop - RPSC) with four population densities of arugula (40, 60, 80 and 100% of the RPSC). The recommended population densities for sole crops of carrot and arugula are 500,000 and 1,000,000 plants per hectare, respectively. All treatments were fertilized with hairy woodrose (Merremia aegyptia L.), a spontaneous species of ...

International Journal of Current Microbiology and Applied Sciences

Dodiya Trupti Polytechnic in Agriculture

Academic Research Journal of Agricultural Science and Research

midekesa chala , Ashenafi Nigussie

A field experiment was conducted at wondo genet Agriculture research center under the irrigated conditions to diversify onion based farming system by inclusion of rosemary for additional cash generation and to determine optimum percentage of rosemary in onion rosemary based farming system during the two successive seasons of 2014 and 2015. The experiment comprised of six treatments in additive series: sole onion (250000 plants ha-1), sole rosemary (83333 plants ha-1) and 4 onion-rosemary intercropping mix- proportion: 100 onion:80 rosemary, 100 onion:60 rosemary, 100 onion:40 rosemary and 100 onion:20 rosemary, using randomized complete block design with four replications. Analysis of variance revealed that; intercropping of onion with different population densities of rosemary might affect fresh and dry bulb yield; highest bulb fresh yield and dry bulb yield were recorded at solitary cropping than that of intercropped. Regardless of mix proportion, highest (10973 kg/ha) and lowest (7839kg/ha) value of dry bulb yield were recorded at 20% and 80% rosemary intercropped with onion, respectively. Likewise; the essential oil yield of rosemary was significantly influenced by the cropping system; highest essential oil yield was obtained in sole planted than intercropped. In the same way, the essential oil yield of rosemary was affected by different levels of intercropped treatments; highest and lowest Essential oil yields were obtained at 80% and 20% rosemary intercropped with onion. The highest value of Land Equivalent Ratio (1.68) and Monitory Advantage (3956.5) were obtained when onion intercropped with 80 % rosemary population density. Therefore, the inclusion of onion with 80% a rosemary intercropping scheme raised yield advantage over the single crop per unit area and year as revealed by the highest total LER, and monetary advantage.

PLANT ARCHIVES

Akansha Verma

Behzad Shokati* and Saeid Zehtab-Salmasi

Azarian Journal of Agriculture (AJA) , Behzad Shokati

A field experiment was conducted based on randomized complete blocks design (RCBD) in three replications during 2011 at the research farm of university of Tabriz, Iran. In this study two medicinal plants, dill (Anethum graveolens L.) and fenugreek (Trigonella foenum-graecum) intercropped at different additive (1:20, 1:40 and 1:60) and different replacement (1:1, 1:2 and 1:3) series. Results showed that dill plant at additive treatment especially in 1:20 and 1:60 series had maximum plant fresh and dry weights, umbels per plant, 1000 seed weight, seeds per plant, biological yield and harvest index. However, fenugreek plant at replacement treatment especially in 1:3 and 1:2 series had maximum biological yield, pod in main stem, pod in branches, seeds per pod, seed weights and grain yield. Fenugreek as a medicinal, forage and legume crop promote dill grows characters and could be an effective plant in intercropping systems.

Journal of Cleaner Production

Yaghoub Raei

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International Journal of Agricultural Science

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Editor-in-Chief

Prof. Konstantinos Arvanitis, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece Prof. Giuseppe Pulighe, CREA Research Centre for Agricultural Policies and Bioeconomy, Via Barberini 36, 00187 Roma, Italy

Members of the Editorial Board

Prof. Luis Loures, Faculty of Agricultural Sciences, University of Applied Sciences of Portalegre, Portugal

Prof. J. Van Mierlo, Vrije Universiteit Brussel, Belgium

Prof. S. Ozdogan, Marmara University, Goztepe Campus, Kuyubasi, Kadikoy, Istanbul, Turkey

Prof. S. Sohrab, Northwestern University, IL, USA

Prof. Kamaruzaman Sopian, Universiti Kebangsaan Malaysia, Malaysia

Prof. Mohammad Nazri Mohd Jaafar, Universiti Teknologi Malaysia, Malaysia

Prof. Mazlan Abdul Wahid, Universiti Teknologi Malaysia, Malaysia

Prof. Jiri Klima, Technical faculty of CZU in Prague, Czech Republic

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The International Journal of Agricultural Science is an open access journal. The goal of this journal is to provide a platform for academicians, researchers and practitioners all over the world to promote, share, and discuss various new issues and developments in all areas of Agricultural Science. Manuscripts are subject to a rigorous and fair peer-review process. Accepted papers will appear online within 3-4 weeks after their submission. The journal publishes papers including but not limited to the following topics:

Agricultural Systems, Agricultural Economics, Agricultural Business, Agricultural Computational Models and Statistics, Agricultural Physics, Agriculture Biochemistry, Agriculture Biotechnology, Agricultural Electrification and Automation, Agriculture extension and rural development, Agro-forestry and Ecotourism, Applied Mechanical Engineering for Agriculture, Agricultural Informatics, Agronomy Horticulture, Agrotourism, Agricultural Resources Agricultural Aquaculture, Biotechnology, Biocomposite Technology, Climate Change and Green Technology, Agricultural Collaborative Engineering, Computational Agricultural Biology, Crop Science, Entomology, Agricultural Biology, Environmental Science and Agriculture, Food science and technology Horticulture, Husbandry Science, Genetics Technology, Agricultural GIS and Remote Sensing, Irrigation and Water Resource Engineering, Land Use Modeling and Crop and Animal System, Natural Hazards and Agriculture, Organic agriculture, Biodiversity, Plant Breeding Genetics and Pathology Plant Nutrition, Plant Protection, Post harvesting Technique and Technology, Agricultural Production Engineering, Renewable Energy for Agriculture, Lakes and Agriculture, Rivers and Agriculture, Sea and Agriculture, Animals, Agricultural Biotechnology, Agricultural Bioengineering, Similar Topics.

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IARAS sends each paper to 3 independent reviewers, experts in the area of the paper. So, each paper will be evaluated by three independent experts according to the following Criteria 1) Relevance to the Journal 2) Scientific - Technical Originality, Potential Impact and Interest for the audience 3) Scientific/Technical Content and Advances beyond The State-Of-The-Art 4) Quality of the Presentation, clarity of the Content 5) Comments for the authors The reviewers are going to indicate their familiarity with the paper's subject, evaluate the paper along the aforementioned criteria. Finally, the Editor-in-Chief or a Member of the Editorial Board will decide whether a paper will be accepted or not. Our Score System will classify the papers as follows * Publish as it is * Consider after Minor Changes * Consider after Major Changes * Reject If the Editor recommends “Publish as it is” the manuscript will undergo a final check by the journal’s editorial office in order to ensure that the manuscript and its review process adhere to the journal’s guidelines and policies. Once this is done, the authors will be notified of the manuscript’s acceptance, and the manuscript will appear in the Articles in Press section of the journal’s website. If the Editor recommends “Consider after Minor Changes,” the authors are notified to prepare and submit a final copy of their manuscript with the required minor changes suggested by the reviewers. The Editor reviews the revised manuscript after the minor changes have been made by the authors. Once the Editor is satisfied with the final manuscript, the manuscript can be accepted. If the Editor recommends “Consider after Major Changes,” the recommendation is communicated to the authors. The authors are expected to revise their manuscripts in accordance with the changes recommended by the reviewers and to submit their revised manuscript in a timely manner. Once the revised manuscript is submitted, the Editor can then make an editorial recommendation which can be “Publish Unaltered,” “Consider after Minor Changes,” or “Reject.” If the Editor recommends rejecting the manuscript, the rejection is immediate. Also, if the majority of the reviewers recommend rejecting the manuscript, the rejection is immediate. All journals published by IARAS are committed to publishing only original material, i.e., material that has neither been published elsewhere, nor is under review elsewhere. IARAS as a participant of CrossCheck uses the iThenticate software to detect instances of overlapping and similar text in submitted manuscripts. Manuscripts that are found to have been plagiarized from a manuscript by other authors, whether published or unpublished, will incur plagiarism sanctions.  

IARAS is committed to maintaining high standards through a rigorous peer-review together with strict ethical policies. Any infringements of professional ethical codes, such as plagiarism, fraudulent use of data, bogus claims of authorship, should be taken very seriously by the editors with zero tolerance. IARAS follows the Code of Conduct of the Committee on Publication Ethics (COPE), and follows the COPE Flowcharts for Resolving Cases of Suspected Misconduct. ΙARAS follows strictly these GUIDELINES ON GOOD PUBLICATION PRACTICE (from COPE):  https://publicationethics.org/files/u7141/1999pdf13.pdf

The submitted manuscript should not have been previously published in any form and must not be currently under consideration for publication elsewhere. Please find further information on Publication Ethics at "Editorial Workflow" (Menu item "Editorial Workflow")

Terms of Collaboration

1). COPYRIGHT: By submitting a paper the author understands that its copyright is transferred to IARAS. IARAS may publish it at their discretion and the author may not resubmit it anywhere else, and that includes other IARAS publications. Based on this copyright transfer of the paper, IARAS is entitled to publish the paper to its journals. 2). After the Review process is completed by the Assoc. Editor and the Reviewers, it is no longer allowed for the authors to withdraw their paper. 3). PEER REVIEW: All submitted papers are subject to strict peer-review process by at least three international reviewers that are experts in the area of the particular paper. The factors that are taken into account in review are relevance, soundness, significance, originality, readability and language. The possible decisions include acceptance, acceptance with revisions, or rejection. 4). If authors are encouraged to revise and resubmit a submission, there is no guarantee that the revised submission will be accepted. 5). Rejected articles will not be re-reviewed. 6). Articles may be rejected without review if they are obviously not suitable for publication. 7). The paper acceptance is constrained by such legal requirements as shall then be in force regarding libel, copyright infringement and plagiarism. 8). The reviewers evaluate manuscripts for their intellectual content without regard to race, gender, sexual orientation, religious belief, ethnic origin, citizenship, or political philosophy of the authors. 9). The staff must not disclose any information about a submitted manuscript to anyone other than the corresponding author, reviewers, other editorial advisers, and the publisher, as appropriate. 10). Reviews should be conducted objectively. Personal criticism of the author is inappropriate. Referees should express their views clearly with supporting arguments. 11). Peer review assists the publisher in making editorial decisions and through the editorial communications with the experts form the scientific board ant the author may also assist the author in improving the paper. 12). Manuscripts received for review are treated as confidential documents and are reviewed by anonymous staff. 13). A reviewer should also call to the publisher's attention any substantial similarity or overlap between the manuscript under consideration and any other published paper of which they have personal knowledge. 14). Authors of contributions and studies research should present an accurate account of the work performed as well as an objective discussion of its significance. 15). A paper should contain sufficient detail and references to permit others to replicate the work. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable. 16). The authors should ensure that they have written entirely original works, and if the authors have used the work and/or words of others that this has been appropriately cited or quoted. 17). Submitting the same manuscript to more than one publication concurrently constitutes unethical publishing behaviour and is unacceptable. 18). Authorship should be limited to those who have made a significant contribution to the conception, design, execution, or interpretation of the reported study.

19).  All sources of financial support for the project should be disclosed. 20). Authors should make themselves aware of the paper processing charges and registration fees of the publication they submit a paper to by reading the respective sections.

21). The IARAS Open Access Journals do not require from the authors to submit any copyright form. An author can withdraw a paper until 30 days after the paper submission. After these 30 days no withdrawal is allowed. It is the discretion to the Publisher to publish a paper with or without fees..

*MANAGEMENT: Our Editor-in-Chief manages this Journal. Receives the articles from IARAS, checks them, rejects those that are not appropriate and forwards to reviewers whatever he believes that deserves an evaluation via the system of peer review. Co-Editors-in-Chief have an Advisory Role. * OWNERSHIP: IARAS Publications Ltd.

International Journal of Agricultural Science currently has an acceptance rate of 35%.

310  105 EUR /paper until December 31, 2024

IARAS Journals are Open Access journal accessible for free on the Internet. We guarantee that no university library or individual reader will ever have to buy a subscription or pay any pay-per-view fees to access articles in the electronic version of the journal. There is hence no revenue at IARAS neither from the sale of subscriptions to the electronic version of the journal nor from pay-per-view fees. Yet, the online publication process does involve costs including those pertaining to setup and maintenance of the publication infrastructure, routine operation of the journal, processing of manuscripts through peer-reviews, editing, publishing, maintaining the scholarly record, and archiving. To cover these costs, the journals depends on Publication Fees. Publication Fees are due when a manuscript has been accepted for publication.

Food and Nutrition Science - An International Journal: 310 105 EUR /paper until December 31, 2024 International Journal of Agricultural Science: 310 105 EUR /paper until December 31, 2024 International Journal of Applied Physics: 310 105 EUR /paper until December 31, 2024 International Journal of Biochemistry Research: 310 105 EUR /paper until December 31, 2024 International Journal of Biology and Biomedicine: 310 105 EUR /paper until December 31, 2024 International Journal of Chemistry and Chemical Engineering Systems: 310 105 EUR /paper until December 31, 2024 International Journal of Circuits and Electronics: 310 105 EUR /paper until December 31, 2024 International Journal of Communications: 310 105 EUR /paper until December 31, 2024 International Journal of Computers: 310 105 EUR /paper until December 31, 2024 International Journal of Control Systems and Robotics: 310 105 EUR /paper until December 31, 2024 International Journal of Cultural Heritage: 310 105 EUR /paper until December 31, 2024 International Journal of Economics and Management Systems: 310 105 EUR /paper until December 31, 2024 International Journal of Education and Learning Systems: 310 105 EUR /paper until December 31, 2024 Journal of Electromagnetics: 310 105 EUR /paper until December 31, 2024 International Journal of Environmental Science: 310 105 EUR /paper until December 31, 2024 International Journal of Instrumentation and Measurement: 310 105 EUR /paper until December 31, 2024 International Journal of Internet of Things and Web Services: 310 105 EUR /paper until December 31, 2024 International Journal of Mathematical and Computational Methods: 310 105 EUR /paper until December 31, 2024 International Journal of Mechanical Engineering: 310 105 EUR /paper until December 31, 2024 International Journal of Medical Physiology: 310 105 EUR /paper until December 31, 2024 International Journal of Power Systems: 310 105 EUR /paper until December 31, 2024 International Journal of Renewable Energy Sources: 310 105 EUR /paper until December 31, 2024 International Journal of Signal Processing: 310 105 EUR /paper until December 31, 2024 International Journal of Theoretical and Applied Mechanics: 310 105 EUR /paper until December 31, 2024 International Journal of Tourism: 310 105 EUR /paper until December 31, 2024 International Journal of Transportation Systems: 310 105 EUR /paper until December 31, 2024 International Journal of Veterinary Medicine: 310 105 EUR /paper until December 31, 2024

Except the fees, IARAS does not have any other source of Revenue. IARAS does not have subscriptions, does not have advertising revenue, does not sell reprints, and does not have any other institutional or organizational support. Publishing fees or waiver status should not influence editorial decision making. Sometimes, excellent papers might be published without fees, especially if the comments from the three Reviewers are that the article does not need corrections and can be published "as it is" or if the authors have special financial difficulties. IARAS does not have Direct Marketing Activities

So far for the "International Journal of Agricultural Science" we have used the following Reviewers

Giri Kattel José Nunes Corina Carranca Lucija Foglar Gholam Khodakaramian Helena Coumoulos Bahar Razavi Ali Gholami Rusu Teodor Sadia Iqbal Magesh Sivan Iustina Popescu George Suciu Bharti Negi

Should you wish to be notified about our journals, conferences, research projects, publications and other important activities you can subscribe to IARAS Newsletter.  Enter your email address below:

IARAS is partnering with Portico to preserve collections from its Digital Archives products. IARAS’s Portico participation means that there will be uninterrupted access to the historical content in these collections. IARAS launched its Digital Archives in 2016 as part of an ongoing initiative to digitize important primary documents of historical source materials used by scholars and students. EBSCO will be taking part in Portico’s D-Collection Preservation Service which preserves digitized historic collections on behalf of participating publishers. This service is solely supported by publishers that have committed their collections to the archive and more than 120 d-collections are preserved in Portico today.

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Manuscripts should be submitted by one of the authors of the manuscript through the online manuscript submission system:  Paper Submission

Current e-mail addresses must be provided for all suggested reviewers.

Regardless of the source of the word-processing tool, only electronic MS-Word or PDF or LaTeX  files can be submitted through the online submission system:  Paper Submission

Relevant Journals

 Publisher : Timeline Publication Pvt. Ltd., Click ISSN (Online) : 2348 – 3997

Welcome to ijras.

Posted in ijras

International Journal of Research in Agricultural Sciences

"Submissions Open For Vol. 11,Issue 5, Sept. - Oct., 2024"

IJ RAS is an international academic online journal which gains a foothold in India, Asia . It aims to promote the research in the feild of agriculture. The focus is to publish quality papers on state-of-the-art of Agriculture. Submitted papers will be reviewed by technical committees of the Journal and Association. All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing.

IJRAS covers all the areas related to agriculture like (not limited to): , This email address is being protected from spambots. You need JavaScript enabled to view it. (Authors are requested to send their papers to both above mentioned email addresses) Final Paper Submission should be done at E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. , submit2ij @yahoo.in Reviewer Response should be done at E-mail: editor@ij .org Any Query response should be done at E-mail: info@ij .org , submit2 @gmail.com

Our Journals

International Journal of Electronics Communication and Computer Engineering ISSN(Online): 2249 - 071X ISSN (Print) : 2278 – 4209 www.ijecce.org Submissions open	International Journal of Agriculture Innovations and Research ISSN(Online) : 2319 – 1473 www.ijair.org Submissions open	International Journal of Innovation in Science and Mathematics ISSN : 2347 – 9051 www.ijism.org Submissions open
International Journal of Engineering Innovations and Research ISSN(Online) : 2277 – 5668 www.ijeir.org Submissions are open.	International Journal of Artificial Intelligence and Mechatronics ISSN(Online) : 2320 – 5121 www.ijaim.org Submissions open	International Journal of Research in Agricultural Sciences ISSN(Online) : 2348 – 3997 www.ijras.org Submissions open

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International Journal of Agricultural Research

International Journal of Agricultural Research is a peer-reviewed scientific journal dedicated to publish high quality research work in the field of agricultural sciences. Scope of the journal includes: Agronomy, crop physiology, crop science, horticulture, plant protection, breeding genetics and pathology, soil and environmental sciences, plant nutrition, rural development, agricultural ecology, agricultural economics, animal science, forestry, marine lives and utilization science of agricultural resources. International Journal of Agricultural Research now accepting new submissions. Submit your best paper via online submission system .

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Recent articles, quality evaluation of commercial fish ponds in uli, anambra state and their health implications, analysis of agro-ecological factors related to the prevalence and diversity of badnavirus in the banana production areas of burkina faso, effect of phthalates from plastic culture materials on the growth and survival of african catfish, integrated management of mycogone perniciosa causing wet bubble disease of white button mushroom ( agaricus bisporus ) in kashmir, control of postharvest anthracnose of mango caused by colletotrichum gloeosporioides penz by the use of bio-pesticides.

The University of Chicago The Law School

Abrams environmental law clinic—significant achievements for 2023-24, protecting our great lakes, rivers, and shorelines.

The Abrams Clinic represents Friends of the Chicago River and the Sierra Club in their efforts to hold Trump Tower in downtown Chicago accountable for withdrawing water illegally from the Chicago River. To cool the building, Trump Tower draws water at high volumes, similar to industrial factories or power plants, but Trump Tower operated for more than a decade without ever conducting the legally required studies to determine the impact of those operations on aquatic life or without installing sufficient equipment to protect aquatic life consistent with federal regulations. After the Clinic sent a notice of intent to sue Trump Tower, the State of Illinois filed its own case in the summer of 2018, and the Clinic moved successfully to intervene in that case. In 2023-24, motions practice and discovery continued. Working with co-counsel at Northwestern University’s Pritzker Law School’s Environmental Advocacy Center, the Clinic moved to amend its complaint to include Trump Tower’s systematic underreporting each month of the volume of water that it intakes from and discharges to the Chicago River. The Clinic and co-counsel addressed Trump Tower’s motion to dismiss some of our clients’ claims, and we filed a motion for summary judgment on our claim that Trump Tower has committed a public nuisance. We also worked closely with our expert, Dr. Peter Henderson, on a supplemental disclosure and on defending an additional deposition of him. In summer 2024, the Clinic is defending its motion for summary judgment and challenging Trump Tower’s own motion for summary judgment. The Clinic is also preparing for trial, which could take place as early as fall 2024.

Since 2016, the Abrams Clinic has worked with the Chicago chapter of the Surfrider Foundation to protect water quality along the Lake Michigan shoreline in northwest Indiana, where its members surf. In April 2017, the U. S. Steel plant in Portage, Indiana, spilled approximately 300 pounds of hexavalent chromium into Lake Michigan. In January 2018, the Abrams Clinic filed a suit on behalf of Surfrider against U. S. Steel, alleging multiple violations of U. S. Steel’s discharge permits; the City of Chicago filed suit shortly after. When the US government and the State of Indiana filed their own, separate case, the Clinic filed extensive comments on the proposed consent decree. In August 2021, the court entered a revised consent decree which included provisions advocated for by Surfrider and the City of Chicago, namely a water sampling project that alerts beachgoers as to Lake Michigan’s water quality conditions, better notifications in case of future spills, and improvements to U. S. Steel’s operations and maintenance plans. In the 2023-24 academic year, the Clinic successfully litigated its claims for attorneys’ fees as a substantially prevailing party. Significantly, the court’s order adopted the “Fitzpatrick matrix,” used by the US Attorney’s Office for the District of Columbia to determine appropriate hourly rates for civil litigants, endorsed Chicago legal market rates as the appropriate rates for complex environmental litigation in Northwest Indiana, and allowed for partially reconstructed time records. The Clinic’s work, which has received significant media attention, helped to spawn other litigation to address pollution by other industrial facilities in Northwest Indiana and other enforcement against U. S. Steel by the State of Indiana.

In Winter Quarter 2024, Clinic students worked closely with Dr. John Ikerd, an agricultural economist and emeritus professor at the University of Missouri, to file an amicus brief in Food & Water Watch v. U.S. Environmental Protection Agency . In that case pending before the Ninth Circuit, Food & Water Watch argues that US EPA is illegally allowing Concentrated Animal Feeding Operations, more commonly known as factory farms, to pollute waterways significantly more than is allowable under the Clean Water Act. In the brief for Dr. Ikerd and co-amici Austin Frerick, Crawford Stewardship Project, Family Farm Defenders, Farm Aid, Missouri Rural Crisis Center, National Family Farm Coalition, National Sustainable Agriculture Coalition, and Western Organization of Resource Councils, we argued that EPA’s refusal to regulate CAFOs effectively is an unwarranted application of “agricultural exceptionalism” to industrial agriculture and that EPA effectively distorts the animal production market by allowing CAFOs to externalize their pollution costs and diminishing the ability of family farms to compete. Attorneys for the litigants will argue the case in September 2024.

Energy and Climate

Energy justice.

The Abrams Clinic supported grassroots organizations advocating for energy justice in low-income communities and Black, Indigenous, and People of Color (BIPOC) communities in Michigan. With the Clinic’s representation, these organizations intervened in cases before the Michigan Public Service Commission (MPSC), which regulates investor-owned utilities. Students conducted discovery, drafted written testimony, cross-examined utility executives, participated in settlement discussions, and filed briefs for these projects. The Clinic’s representation has elevated the concerns of these community organizations and forced both the utilities and regulators to consider issues of equity to an unprecedented degree. This year, on behalf of Soulardarity (Highland Park, MI), We Want Green, Too (Detroit, MI), and Urban Core Collective (Grand Rapids, MI), Clinic students engaged in eight contested cases before the MPSC against DTE Electric, DTE Gas, and Consumers Energy, as well as provided support for our clients’ advocacy in other non-contested MPSC proceedings.

The Clinic started this past fall with wins in three cases. First, the Clinic’s clients settled with DTE Electric in its Integrated Resource Plan case. The settlement included an agreement to close the second dirtiest coal power plant in Michigan three years early, $30 million from DTE’s shareholders to assist low-income customers in paying their bills, and $8 million from DTE’s shareholders toward a community fund that assists low-income customers with installing energy efficiency improvements, renewable energy, and battery technology. Second, in DTE Electric’s 2023 request for a rate hike (a “rate case”), the Commission required DTE Electric to develop a more robust environmental justice analysis and rejected the Company’s second attempt to waive consumer protections through a proposed electric utility prepayment program with a questionable history of success during its pilot run. The final Commission order and the administrative law judge’s proposal for final decision cited the Clinic’s testimony and briefs. Third, in Consumers Electric’s 2023 rate case, the Commission rejected the Company’s request for a higher ratepayer-funded return on its investments and required the Company to create a process that will enable intervenors to obtain accurate GIS data. The Clinic intends to use this data to map the disparate impact of infrastructure investment in low-income and BIPOC communities.

In the winter, the Clinic filed public comments regarding DTE Electric and Consumers Energy’s “distribution grid plans” (DGP) as well as supported interventions in two additional cases: Consumers Energy’s voluntary green pricing (VGP) case and the Clinic’s first case against the gas utility DTE Gas. Beginning with the DGP comments, the Clinic first addressed Consumers’s 2023 Electric Distribution Infrastructure Investment Plan (EDIIP), which detailed current distribution system health and the utility’s approximately $7 billion capital project planning ($2 billion of which went unaccounted for in the EDIIP) over 2023–2028. The Clinic then commented on DTE Electric’s 2023 DGP, which outlined the utility’s opaque project prioritization and planned more than $9 billion in capital investments and associated maintenance over 2024–2028. The comments targeted four areas of deficiencies in both the EDIIP and DGP: (1) inadequate consideration of distributed energy resources (DERs) as providing grid reliability, resiliency, and energy transition benefits; (2) flawed environmental justice analysis, particularly with respect to the collection of performance metrics and the narrow implementation of the Michigan Environmental Justice Screen Tool; (3) inequitable investment patterns across census tracts, with emphasis on DTE Electric’s skewed prioritization for retaining its old circuits rather than upgrading those circuits; and (4) failing to engage with community feedback.

For the VGP case against Consumers, the Clinic supported the filing of both an initial brief and reply brief requesting that the Commission reject the Company’s flawed proposal for a “community solar” program. In a prior case, the Clinic advocated for the development of a community solar program that would provide low-income, BIPOC communities with access to clean energy. As a result of our efforts, the Commission approved a settlement agreement requiring the Company “to evaluate and provide a strawman recommendation on community solar in its Voluntary Green Pricing Program.” However, the Company’s subsequent proposal in its VGP case violated the Commission’s order because it (1) was not consistent with the applicable law, MCL 460.1061; (2) was not a true community solar program; (3) lacked essential details; (4) failed to compensate subscribers sufficiently; (5) included overpriced and inflexible subscriptions; (6) excessively limited capacity; and (7) failed to provide a clear pathway for certain participants to transition into other VGP programs. For these reasons, the Clinic argued that the Commission should reject the Company’s proposal.

In DTE Gas’s current rate case, the Clinic worked with four witnesses to develop testimony that would rebut DTE Gas’s request for a rate hike on its customers. The testimony advocated for a pathway to a just energy transition that avoids dumping the costs of stranded gas assets on the low-income and BIPOC communities that are likely to be the last to electrify. Instead, the testimony proposed that the gas and electric utilities undertake integrated planning that would prioritize electric infrastructure over gas infrastructure investment to ensure that DTE Gas does not over-invest in gas infrastructure that will be rendered obsolete in the coming decades. The Clinic also worked with one expert witness to develop an analysis of DTE Gas’s unaffordable bills and inequitable shutoff, deposit, and collections practices. Lastly, the Clinic offered testimony on behalf of and from community members who would be directly impacted by the Company’s rate hike and lack of affordable and quality service. Clinic students have spent the summer drafting an approximately one-hundred-page brief making these arguments formally. We expect the Commission’s decision this fall.

Finally, both DTE Electric and Consumers Energy have filed additional requests for rate increases after the conclusion of their respective rate cases filed in 2023. On behalf of our Clients, the Clinic has intervened in these cases, and clinic students have already reviewed thousands of pages of documents and started to develop arguments and strategies to protect low-income and BIPOC communities from the utility’s ceaseless efforts to increase the cost of energy.

Corporate Climate Greenwashing

The Abrams Environmental Law Clinic worked with a leading international nonprofit dedicated to using the law to protect the environment to research corporate climate greenwashing, focusing on consumer protection, green financing, and securities liability. Clinic students spent the year examining an innovative state law, drafted a fifty-page guide to the statute and relevant cases, and examined how the law would apply to a variety of potential cases. Students then presented their findings in a case study and oral presentation to members of ClientEarth, including the organization’s North American head and members of its European team. The project helped identify the strengths and weaknesses of potential new strategies for increasing corporate accountability in the fight against climate change.

Land Contamination, Lead, and Hazardous Waste

The Abrams Clinic continues to represent East Chicago, Indiana, residents who live or lived on or adjacent to the USS Lead Superfund site. This year, the Clinic worked closely with the East Chicago/Calumet Coalition Community Advisory Group (CAG) to advance the CAG’s advocacy beyond the Superfund site and the adjacent Dupont RCRA site. Through multiple forms of advocacy, the clinics challenged the poor performance and permit modification and renewal attempts of Tradebe Treatment and Recycling, LLC (Tradebe), a hazardous waste storage and recycling facility in the community. Clinic students sent letters to US EPA and Indiana Department of Environmental Management officials about how IDEM has failed to assess meaningful penalties against Tradebe for repeated violations of the law and how IDEM has allowed Tradebe to continue to threaten public and worker health and safety by not improving its operations. Students also drafted substantial comments for the CAG on the US EPA’s Lead and Copper Rule improvements, the Suppliers’ Park proposed cleanup, and Sims Metal’s proposed air permit revisions. The Clinic has also continued working with the CAG, environmental experts, and regulators since US EPA awarded $200,000 to the CAG for community air monitoring. The Clinic and its clients also joined comments drafted by other environmental organizations about poor operations and loose regulatory oversight of several industrial facilities in the area.

Endangered Species

The Abrams Clinic represented the Center for Biological Diversity (CBD) and the Hoosier Environmental Council (HEC) in litigation regarding the US Fish and Wildlife Service’s (Service) failure to list the Kirtland’s snake as threatened or endangered under the Endangered Species Act. The Kirtland’s snake is a small, secretive, non-venomous snake historically located across the Midwest and the Ohio River Valley. Development and climate change have undermined large portions of the snake’s habitat, and populations are declining. Accordingly, the Clinic sued the Service in the US District Court for the District of Columbia last summer over the Service’s denial of CBD’s request to have the Kirtland’s snake protected. This spring, the Clinic was able to reach a settlement with the Service that requires the Service to reconsider its listing decision for the Kirtland’s snake and to pay attorney fees.

The Clinic also represented CBD in preparation for litigation regarding the Service’s failure to list another species as threatened or endangered. Threats from land development and climate change have devastated this species as well, and the species has already been extirpated from two of the sixteen US states in its range. As such, the Clinic worked this winter and spring to prepare a notice of intent (NOI) to sue the Service. The Team poured over hundreds of FOIA documents and dug into the Service’s supporting documentation to create strong arguments against the Service in the imminent litigation. The Clinic will send the NOI and file a complaint in the next few months.

Students and Faculty

Twenty-four law school students from the classes of 2024 and 2025 participated in the Clinic, performing complex legal research, reviewing documents obtained through discovery, drafting legal research memos and briefs, conferring with clients, conducting cross-examination, participating in settlement conferences, and arguing motions. Students secured nine clerkships, five were heading to private practice after graduation, and two are pursuing public interest work. Sam Heppell joined the Clinic from civil rights private practice, bringing the Clinic to its full complement of three attorneys.

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939

The International Journal of Agriculture and Environmental Research (IJAER) is a multidisciplinary journal that publishes empirical and theoretical Papers/Articles on all fields of Agriculture and Environmental research. IJAER is an Open Access journal. This means that it uses a funding model that does not charge readers or their institutions for access. Readers may freely read, download, copy, distribute, print, search, or link to the full texts of articles. All manuscripts submitted, including symposium papers, will be peer reviewed by qualified scholars assigned by the editorial board.

• Publication Frequency: bi-monthly
• Publishing Language: English
• Crossref DOI: https://doi.org/10.51193/ijaer
• Impact Factor: 5.98

MISSION VS VISION

IJAER is pleased to offer free access to online publishing. We are committed to promote academic exchanges and progress. Publishing with IJAER will provide high visibility of your research work and make you know the latest academic trends. The aim of the International Journal of Agriculture and Environmental Research (IJAER) is to foster the growth of educational, scientific and industrial research activities among engineers and to provide a medium for mutual communication between the world academia and the industry on the one hand, and the world scientific community on the other. Our philosophy is to map new frontiers in emerging and developing technology areas in research, industry and governance, and to link with centres of excellence worldwide to provide authoritative coverage and references in focused and specialist fields. Join us!

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International Journal of Research Studies in Agricultural Sciences

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Editor in Chief	:	Mohammad Valipour
ISSN No. (Online)	:	2454–6224
Publication Frequency	:	12 Issues per Year
Submit Paper at	:
Language of Publication	:	English

Submissions are open for Volume-10, Issue-3

Paper Submission	:	20 March, 2024
Author Notification	:	On or before March, 2024
Issue Publication	:	30 March, 2024

Introduction

Agriculture is the cultivation of animals, plants, fungi, and other life forms for food, fiber, biofuel, medicinal and other products used to sustain and upgrade human life. Agriculture was the key improvement in the ascent of inactive human progress, whereby cultivating of trained species made nourishment surpluses that sustained the advancement of development. The investigation of agribusiness is known as horticultural science.

International Journal of Research Studies in Agricultural Sciences (IJRSAS) is an internationally peer reviewed, open access journal that publishes current research in Agricultural Sciences and its related fields. We publish recent advancements in disciplines such as Agribusiness, Agricultural science, Agroecology, Agroforestry, Agricultural engineering, Mechanised agriculture, Intensive crop farming, Organic farming etc.

We invite manuscripts of invite submissions as Original articles, research articles, review article, short reports and editorial articles.

International Journal of Agriculture and Biology

Journal home, aims and scope.

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About Journal

Frequency	02 Volumes per year and 06 issues in each volume
Areas Covered	Agriculture, Biology, Multidisciplinary
Accepted Language	English Only
Type of Articles	Full Length Article, Review Article, Short Communication

Journal Cover

Internation Journal of Agriculture and Biology

International Journal of Agriculture and Biology (IJAB) is publishes peer reviewed papers on all facets of agriculture and biology. The journal features reviews (both solicited and submitted), full-length research articles, and short communications. Submissions must be original and not under consideration for publication elsewhere. After an initial evaluation by the Editor-in-Chief and a Subject Editor, papers are reviewed by two experts in the relevant fields. The Subject Editor makes the final publication decision based on the referees' recommendations.

Editors-in-Chief: Dr. Abdul Wahid

Publishing Model: Open Access .

Mission of IJAB

The International Journal of Agriculture and Biology (IJAB) is an esteemed publication dedicated to showcasing significant contributions in agriculture and biology through a diverse collection of scholarly articles. Our mission is to foster scientific discovery and innovation by publishing cutting-edge research.

The aim of the IJAB is to advance knowledge and promote innovation in the fields of agriculture and biology. By providing a comprehensive platform for the dissemination of high-quality, peer-reviewed research, the journal seeks to foster innovation and knowledge sharing across all aspects of plant and animal agriculture, as well as allied biological sciences. The journal aspires to be a leading source of cutting-edge research and insights that address critical challenges, promote sustainable practices, and enhance productivity and resilience in agricultural systems worldwide. Through this, we aim to contribute significantly to global food security, environmental sustainability, and the advancement of agricultural science and technology.

IJAB publishes high-quality, peer-reviewed research that advances the fields of agriculture and biology. The journal covers all aspects of plant and animal agriculture, as well as allied biological sciences. It focuses on fostering innovation and disseminating knowledge to address global challenges. The scope of the journal includes, but is not limited to, the following fields:

• Crop Production and Management
• Plant Genetics and Biotechnology
• Pest and Disease Management
• Postharvest Technology
• Animal Husbandry
• Animal Genetics and Breeding
• Veterinary Science
• Sustainable Agriculture
• Climate Change and Agriculture
• Agroecology
• Food and Nutritional Science
• Precision Agriculture
• Applications of Information Technology and AI in Agriculture
• Agricultural Economics and Rural Development

By encompassing these diverse areas, IJAB aims to be a leading platform for researchers and practitioners to share their findings and contribute to the advancement of agricultural and biological sciences.

Article Published in IJAB

IJAB publishes three types of articles:

• Full-Length Articles: These are in-depth research studies presenting clear hypotheses, robust methodologies, and groundbreaking results.
• Review Articles: These provide comprehensive examinations of current scientific literature, offering critical insights and perspectives.
• Short Communications: These are brief reports highlighting the latest scientific findings and advancements.

Processing Charges

Upon the acceptance of the article, the authors are required to pay a one-time Article Processing Charge (APCs) of US $200 for each accepted paper for foreign authors and Pak. Rs. 12,000 as bank draft for Pakistani authors. Payment methods for international and Pakistani authors are detailed below:

• Through Western Union Bank addressed to: Friends Science Publishers. Send the bank receipt via e-mail or provide the Money Transfer Control Number (10 digits) and sender’s full details.
• Via Friends Science Publishers’ International Bank Account Number (IBAN): PK47HABB0001427901881203 at Habib Bank Limited, Bankers Street, Near Estate Care Department, University of Agriculture Faisalabad, Pakistan. Swift Code HABBPKKA. Proof of all transactions is required.

Pak. Rs. 12,000 as bank draft in favor of “Friends Science Publishers” .

Open Access Statement

IJAB embraces the principles of Open Access, under the CC BY license (http://creativecommons.org/licenses/by/4.0/), ensuring unrestricted and free access to all published articles. Our model facilitates the immediate global dissemination of research without any financial or legal barriers, promoting the advancement of knowledge across the scientific community and the public at large.

Cost Coverage: Publication costs are borne by the authors, their respective institutions, or designated research funding. This approach supports the sustainability of the Open Access model while allowing for the broadest possible distribution of the research.

Copyright Ownership: Authors retain full copyright of their work, granting the journal a license to publish the article. This empowers authors with control over their intellectual property while enabling the free sharing of information.

The views and opinions expressed in the articles published in the IJAB articles are solely those of the respective authors and necessarily reflect the position of Friends Science Publishers or IJAB. While every effort is made to ensure the accuracy of the content, IJAB does not guarantee its currency and is not obliged to update the material.

Upon satisfactory revision, acceptance and receipt of publication charges, the manuscripts are typeset. Proofs are prepared and sent to authors for correction, which should be sent back immediately or within 72 hours at the latest. Only minor corrections (no more than 5% of the data or text) are allowed at this stage. If the corrected proofs are not received by due date, the Editorial Office will reserve the right to make corrections and publish the paper as its final form. Handling of proofs is done electronically.

Final published articles are available online at: www.fspublishers.org approximately15‒20 days before the official publication date. Corresponding author will receive a PDF version; no hard copies are provided.

Online : 1814-9596 Print : 1560-8530

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International Journal of Research in Agriculture and Forestry

E-ISSN : 2394-5915 ; P-ISSN : 2394-5907

Email ID : [email protected]

Welcome to ijraf.

The International Journal of Research in Agriculture and Forestry (IJRAF) aims at bringing about a perfect blend of two important domains AGRICULTURE and FORESTRY through launching a new international journal. It publishes research articles, papers, reviews, letters, technical reports, case studies and all other communications in domains of Agriculture and Forestry. If Agriculture is a science of cultivating land, Forestry is a science and practice of planting, caring for and managing forests. Both Agriculture and Forestry constitute themselves into AGRO FORESTRY calling for more and more information to be provided by the contributions to be made by authors of diversity.

IJRAF has a mission to publish the papers of the highest quality with innovative ideas on all the subjects relating to Agriculture and Forestry.

IJRAF expects all the articles or papers to be original which are not published previously or submitted to any journal for the consideration for publication. All the articles or papers must adhere to the style and the ethics of the IJRAF. All the articles or papers will be thoroughly reviewed and edited by expert reviewers and language editors before consideration for publication

Recently Published Articles

Luis A. Garcia and Ahmed A. Eldeiry View | Download | 21-34 Kefas, M, Jidauna, S. B, Michael, K. G. and Wasa, G. F View | Download | 13-20 Muhammad Aslam, Tanweer Ahmed and Mrs. Laila Yaseen View | Download | 08-12 Uduji, M.A, Uche, M, Simeon-Ahaotu, V.C, Patricio De Los Rios and Ahaotu, E.O View | Download | 01-07 Information For Authors For Reviewers For Editors Journal Policies Plagiarism Policy Copyright Policy Useful Links Peer Review Process Special Issue Proposal Published Special Issues Conference Proposal Conference Proceedings International Journal of Research Studies in Agricultural Sciences Article processing charges (apc). International Journal of Research Studies in Agricultural Sciences published under ARC Publications is an open access journal and so the authors are requested to pay APC to publish their articles. In open access model, the publication costs of an article are paid from an author's research budget, or by their supporting institution. In order to provide free and immediate access to full text versions of your research articles and high quality publishing services, the authors are charged processing fee. Please note that the authors will be requested for APC only if the paper is accepted for publication. APC covers several expenses of publisher like peer-reviewing, editing, publishing, archiving and other costs associated with publication of the articles. Authors from High Income Countries 450 USD / 425 Euro / 365 GBP Authors from Upper Middle Income Countries 200 USD / 188 Euro / 160 GBP Authors from Lower Middle Income Countries 100 USD / 94 Euro / 80 GBP Authors from Low Income Countries 75 USD / 70 Euro / 60 GBP Please click on this link of Countries list, economy wise - to see which category you belong to. Hard Copies Hard copy of the journal is provided to authors on request. To get one hard copy of the journal the author(s) need to pay 50 USD /47 EURO /40 GBP towards printing and postal charges. The hard copy of the particular issue will only be posted after online publication of the entire issue. The hard copy will be posted through regular postal services which generally take 15 to 20 business days. If authors require speedy delivery of hard copy they can contact our concerned journal team through mail. The charges for speedy delivery will be more compared to regular delivery. We provide reprints of the article for convenience of authors. The minimum number of reprints to be ordered is 100. For more information, please visit our reprints page. 451 IMAGES International Journal of Agronomy and Agricultural Research International Journal of Innovative Approaches in Agricultural Research International Journal of Agronomy and Agricultural Research Current Agriculture Research Journal International Journal of Agronomy and Agricultural Research International Journal of Agricultural and Applied Sciences (IJAAS) VIDEO International Agricultural Research Institutes World Agriculture Day Fête internationale de la science : agriculture, alimentation environnement The Application of Artificial Intelligence (AI) in Agriculture Strengthening Africa’s pastoral food systems transformation in the face of climate change Internship in Food Processing + International Journal Research Paper Publishing (In English) COMMENTS International Journal of Research Studies in Agricultural Sciences Introduction. Agriculture is the cultivation of animals, plants, fungi, and other life forms for food, fiber, biofuel, medicinal and other products used to sustain and upgrade human life. Agriculture was the key improvement in the ascent of inactive human progress, whereby cultivating of trained species made nourishment surpluses that sustained ... International Journal of Research Studies in Agricultural Sciences International Journal of Research Studies in Agricultural Sciences. Volume-8 Issue-1, 2022. Mechanisms of Host Plant Resistance in Chickpea (Cicer arietinum L.) Against Adzuki Bean Beetle (Callosobruchus chinensis L.) Infesetation: A Review. Physiological Responses Non Selective Post Emergence Herbicides to Various Weeds in Peach Field. International Journal of Research Studies in Agricultural Sciences Volume-1, Issue-4. Volume-1, Issue-5. Volume-1, Issue-6. International Journal of Research Studies in Agricultural Sciences provides uninterrupted access to all the published articles with an aim to disseminate the research in the field of agriculture. International Journal of Research Studies in Agricultural Sciences International Journal of Research Studies in Agricultural Sciences. Volume-8 Issue-4, 2022. Effects of Nitrogen and Phosphorus Fertilization Rates on Tomato Yield and Partial Factor productivity Under Irrigation Condition in Southern, Ethiopia. Melkamu Hordofa Sigaye, Belstie lulie, Ribka Mekuria3 and Kidist Kebede. Download | Page No : 1-7. Global spread of Conservation Agriculture: International Journal of ABSTRACT. Conservation Agriculture (CA) comprises the practical application of three interlinked principles, namely: no or minimum mechanical soil disturbance, biomass mulch soil cover and crop species diversification, in conjunction with other complementary good agricultural practices of integrated crop and production management. Journal of Agriculture and Food Research The Journal of Agriculture and Food Research is a peer-reviewed open access journal focusing on research in the agricultural and food sciences. The journal welcomes full length research articles, reviews, short communications, perspectives, and commentaries from researchers in academic …. View full aims & scope. $2120. Article publishing charge. International journal of research studies in agricultural sciences Title proper: International journal of research studies in agricultural sciences. Abbreviated key-title: Int. j. res. stud. agric. sci. Original alphabet of title: Basic roman Integration of Remote Sensing and Machine Learning for Precision ... Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination ... (PDF) International Journal of Research Studies in Agricultural International Journal of Research Studies in Agricultural Sciences (IJRSAS) Page | 1 Competitiveness and Yield Advantage of Carrot-Rosemary Intercropping over Solitary at Wondo Genet, Southern Ethiopia Intercropping is the growing of two or more cultivars simultaneously in the same land by utilizing resources such as soil, water, nutrients and ... International Journal of Agricultural Science The International Journal of Agricultural Science is an open access journal. The goal of this journal is to provide a platform for academicians, researchers and practitioners all over the world to promote, share, and discuss various new issues and developments in all areas of Agricultural Science. ... Authors of contributions and studies ... International Journal of Research Studies in Agricultural Sciences International Journal of Research Studies in Agricultural Sciences (IJRSAS) Editor in Chief: Mohammad Valipour: ISSN No. (Online): 2454-6224: Publication Frequency: 12 Issues per Year: Submit Paper at: ... International Journal of Research Studies in Agricultural Sciences (IJRSAS) is an internationally peer reviewed, open access journal that ... Mobile phones are not linked to brain cancer, according to a ... A systematic review into the potential health effects from radio wave exposure has shown mobile phones are not linked to brain cancer. The review was commissioned by the World Health Organization ... International Journal of Research Studies in Agricultural Sciences Volume-9 Issue-2, 2023. Determination of the level of pesticide residues, heavy metals and physicochemical compositions of cow's milk in Hawassa city, Ethiopia. International Journal of Research Studies in Agricultural Sciences is an open access journal that publishes high quality articles in the field of agriculture and allied areas. Agricultural modernization and sustainable agriculture: contradictions To address this gap, we draw on evidence from real-life cases in fourteen countries in an attempt to interpret how the two concepts are perceived in very different contexts. These case studies show that different understandings of modern and sustainable agriculture coexist and that agricultural development follows diverse pathways. Agricultural Economics Agricultural Economics is an international journal publishing research results and policy analyses from across disciplines, from food consumption to land use. ... most previous weather-related copula studies focused on yield data of one or two crops, and these analyses were conducted for each crop individually. ... most monetary losses in ... International Journal of Research in Agricultural Sciences IJRAS Index Copernicus Value (ICV):78.25. IJRAS is an international academic online journal which gains a foothold in India, Asia . It aims to promote the research in the feild of agriculture. The focus is to publish quality papers on state-of-the-art of Agriculture. Submitted papers will be reviewed by technical committees of the Journal and ... International Journal of Agricultural Research Publisher: Academic Journals Inc., USA. International Journal of Agricultural Research is a peer-reviewed scientific journal dedicated to publish high quality research work in the field of agricultural sciences. Scope of the journal includes: Agronomy, crop physiology, crop science, horticulture, plant protection, breeding genetics and ... International journal of research studies in agricultural sciences Abbreviation of International journal of research studies in agricultural sciences. The ISO4 abbreviation of International journal of research studies in agricultural sciences is Int. j. res. stud. agric. sci. . It is the standardised abbreviation to be used for abstracting, indexing and referencing purposes and meets all criteria of the ISO 4 standard for abbreviating names of scientific ... Abrams Environmental Law Clinic—Significant Achievements for 2023-24 Protecting Our Great Lakes, Rivers, and Shorelines The Abrams Clinic represents Friends of the Chicago River and the Sierra Club in their efforts to hold Trump Tower in downtown Chicago accountable for withdrawing water illegally from the Chicago River. To cool the building, Trump Tower draws water at high volumes, similar to industrial factories or power plants, but Trump Tower operated for ... Submit Manuscript Article Details* : * Journal Title. International Journal of Research Studies in Agricultural Sciences International Journal of Innovative Studies in Aquatic Biology and Fisheries International Journal of Forestry and Horticulture International Journal of Managerial Studies and Research International Journal of Advanced Research in Chemical ... International Journal of Agriculture and Environmental Research The International Journal of Agriculture and Environmental Research (IJAER) is a multidisciplinary journal that publishes empirical and theoretical Papers/Articles on all fields of Agriculture and Environmental research. IJAER is an Open Access journal. This means that it uses a funding model that does not charge readers or their institutions ... International Journal of Research Studies in Agricultural Sciences International Journal of Research Studies in Agricultural Sciences is an open access journal that publishes high quality articles in the field of agriculture and allied areas. Check Paper Status. ... International Journal of Research Studies in Agricultural Sciences (IJRSAS) is an internationally peer reviewed, open access journal that ... The Properties of Agricultural Waste Particle Composite Reinforced with The production of particle composites using agricultural waste materials is an area of significant research interest. 1ngeneral the properties of agricultural waste particle composites are considered to be inferior to those of commercial particleboards. In this study, the inclusion of woven cotton fabric has been used to overcome this drawback. International Journal of Research Studies in Agricultural Sciences ARC journal of agriculture covers vast topics that are related to current day agriculture practices providing latest developments. Check Paper Status. ... International Journal of Research Studies in Agricultural Sciences Scope of Journal. The scope of the journal includes but not limited to. Agribusiness Agricultural science International Journal of Agriculture and Biology Aims and Scope. The aim of the IJAB is to advance knowledge and promote innovation in the fields of agriculture and biology. By providing a comprehensive platform for the dissemination of high-quality, peer-reviewed research, the journal seeks to foster innovation and knowledge sharing across all aspects of plant and animal agriculture, as well as allied biological sciences. IJRAF Welcome to IJRAF. The International Journal of Research in Agriculture and Forestry (IJRAF) aims at bringing about a perfect blend of two important domains AGRICULTURE and FORESTRY through launching a new international journal. It publishes research articles, papers, reviews, letters, technical reports, case studies and all other communications in domains of Agriculture and Forestry. International Journal of Research Studies in Agricultural Sciences Article Processing Charges (APC) International Journal of Research Studies in Agricultural Sciences published under ARC Publications is an open access journal and so the authors are requested to pay APC to publish their articles. In open access model, the publication costs of an article are paid from an author's research budget, or by their ... essay homework paper phd project resume review speech study thesis