Current - Issue
Original Article
A Review of Federated Machine Learning for Crop Yield Prediction: Sustainable Integration of IOT and Smart Agriculture
J.Jagadeesan1
Dr. R. Nagarajan2
1 Research Scholar, Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India. 2 Assistant Professor, Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 532-543
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502059
References
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6. Ali, N., Mohammed, A., Bais, A., Berraies, S., Ruan, Y., Cuthbert, R.D. and Sangha, J.S., 2024.Field Scale Precision: Predicting Grain Yield of Diverse Wheat Breeding Lines Using High-Throughput UAV Multispectral Imaging. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
7. Ansarifar, J., Wang, L. and Archontoulis, S.V., 2021. An interaction regression model for crop yield prediction. Scientific reports, 11(1), pp.1-14.
8. Arshad, J., Aziz, M., Al-Huqail, A.A., Zaman, M.H.U., Husnain, M., Rehman, A.U. and Shafiq, M., 2022. Implementation of a LoRaWAN based smart agriculture decision support system for optimum crop yield. Sustainability, 14(2), p.827.
9. Ayalew, A.T. and Lohani, T.K., 2023. Prediction of Crop Yield by Support Vector Machine Coupled with Deep Learning Algorithm Procedures in Lower Kulfo Watershed of Ethiopia. Journal of Engineering, 2023(1), p.6675523.
10. Badshah, A., Alkazemi, B.Y., Din, F., Zamli, K.Z. and Haris, M., 2024. Crop Classification and Yield Prediction using Robust Machine Learning Models for Agricultural Sustainability. IEEE Access.
11. Bregaglio, S., Ginaldi, F., Raparelli, E., Fila, G. and Bajocco, S., 2023.Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets. Agricultural Systems, 209, p.103666.
12. Chen, D., Yang, P., Chen, I.R., Ha, D.S. and Cho, J.H., 2024.SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms. arXiv preprint arXiv:2402.10280.
13. Desloires, J., Ienco, D. and Botrel, A., 2023. Out-of-year corn yield prediction at field-scale using Sentinel-2 satellite imagery and machine learning methods. Computers and Electronics in Agriculture, 209, p.107807.
14. Dey, T., Bera, S., Paul, B., De, D., Mukherjee, A. and Buyya, R., 2024. Fly: Femtolet-based edge-cloud framework for crop yield prediction using bidirectional long short-term memory. Software: Practice and Experience.
15. Elavarasan, D. and Vincent, P.D., 2020. Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access, 8, pp.86886-86901
16. Elbasi, E., Zaki, C., Topcu, A.E., Abdelbaki, W., Zreikat, A.I., Cina, E., Shdefat, A. and Saker, L., 2023. Crop prediction model using machine learning algorithms. Applied Sciences, 13(16), p.9288.
17. Fei, S., Hassan, M.A., Xiao, Y., Su, X., Chen, Z., Cheng, Q., Duan, F., Chen, R. and Ma, Y., 2023. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision agriculture, 24(1), pp.187-212.
18. Gharakhanlou, N.M. and Perez, L., 2024. From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change. Science of The Total Environment, 951, p.175764.
19. Guo, Y., Xiao, Y., Hao, F., Zhang, X., Chen, J., de Beurs, K., He, Y. and Fu, Y.H., 2023. Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 124, p.103528.
20. Gupta, A. and Nahar, P., 2023. Classification and yield prediction in smart agriculture system using IoT. Journal of Ambient Intelligence and Humanized Computing, 14(8), pp.10235-10244.
21. Gupta, S., Geetha, A., Sankaran, K.S., Zamani, A.S., Ritonga, M., Raj, R., Ray, S. and Mohammed, H.S., 2022. Machine Learning-and Feature Selection-Enabled Framework for Accurate Crop Yield Prediction. Journal of Food Quality, 2022(1), p.6293985.
22. Haider, S.T., Ge, W., Li, J., Rehman, S.U., Imran, A., Sharaf, M. and Haider, S.M., 2024. An Ensemble Machine Learning Framework for Cotton Crop Yield Prediction Using Weather Parameters: A Case Study of Pakistan. IEEE Access.
23. Hiremani, V. and Devadas, V., 2025. Federated learning for crop yield prediction: A comprehensive review of techniques and applications. Journal of Agriculture and Food Research.
24. Hoque, M.J., Islam, M.S., Uddin, J., Samad, M.A., De Abajo, B.S., Vargas, D.L.R. and Ashraf, I., 2024. Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning. IEEE Access.
25. Hu, T., Zhang, X., Bohrer, G., Liu, Y., Zhou, Y., Martin, J., Li, Y. and Zhao, K., 2023. Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield. Agricultural and Forest Meteorology, 336, p.109458.
26. Idoje, G., Dagiuklas, T. and Iqbal, M., 2023. Federated Learning: Crop classification in a smart farm decentralised network. Smart Agricultural Technology, 5, p.100277.
27. Ilyas, Q.M., Ahmad, M. and Mehmood, A., 2023.Automated estimation of crop yield using artificial intelligence and remote sensing technologies. Bioengineering, 10(2), p.125.
28. Iniyan, S. and Jebakumar, R., 2022. Mutual information feature selection (MIFS) based crop yield prediction on corn and soybean crops using multilayer stacked ensemble regression (MSER). Wireless Personal Communications, 126(3), pp.1935-1964.
29. Islam, M.R., Oliullah, K., Kabir, M.M., Alom, M. and Mridha, M.F., 2023. Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation. Journal of Agriculture and Food Research, 14, p.100880.
30. Jaliyagoda, N., Lokuge, S., Gunathilake, P.M.P.C., Amaratunga, K.S.P., Weerakkody, W.A.P., Bandaranayake, P.C. and Bandaranayake, A.U., 2023. Internet of things (IoT) for smart agriculture: Assembling and assessment of a low-cost IoT system for polytunnels. Plos one, 18(5), p.e0278440.
31. Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., &Uwamahoro, A. (2023). Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize. Agriculture.
32. Li, A., Markovic, M., Edwards, P. and Leontidis, G., 2024. Model pruning enables localized and efficient federated learning for yield forecasting and data sharing. Expert Systems with Applications, 242, p.122847.
33. Li, L., Li, J., Chen, D., Pu, L., Yao, H. and Huang, Y., 2025. VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture. arXiv preprint arXiv:2504.13365.
34. Li, L., Li, J., Chen, D., Pu, L., Yao, H. and Huang, Y., 2025. FedReplay: A Feature Replay Assisted Federated Transfer Learning Framework for Efficient and Privacy-Preserving Smart Agriculture. arXiv preprint arXiv:2511.00269.
35. Mahesh, P. and Soundrapandiyan, R., 2024. Yield prediction for crops by gradient-based algorithms. Plos one, 19(8), p.e0291928.
36. Mamatha, V. and Kavitha, J.C., 2023. Machine learning based crop growth management in greenhouse environment using hydroponics farming techniques. Measurement: Sensors, 25, p.100665.
37. MirhoseiniNejad, S.M., Abbasi-Moghadam, D. and Sharifi, A., 2024.ConvLSTM-ViT: A Deep Neural Network for Crop Yield Prediction Using Earth Observations and Remotely Sensed Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
38. Mitra, A., Beegum, S., Fleisher, D., Reddy, V.R., Sun, W., Ray, C., Timlin, D. and Malakar, A., 2024. Cotton yield prediction: a machine learning approach with field and synthetic data. IEEE Access.
39. Morales, A. and Villalobos, F.J., 2023.Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science, 14, p.1128388.
40. Mughal, F.R., He, J., Das, B., Dharejo, F.A., Zhu, N., Khan, S.B. and Alzahrani, S., 2024. Adaptive federated learning for resource-constrained IoT devices through edge othersintelligence and multi-edge clustering. Scientific Reports, 14(1), p.28746.others Mukherjee, A. and Buyya, R., 2024. Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application. arXiv preprint arXiv:2408.02998.
41. Nejad, S.M.M., Abbasi-Moghadam, D., Sharifi, A., Farmonov, N., Amankulova, K. and Lászlź, M., 2022.Multispectral crop yield prediction using 3D-convolutional neural networks and attention convolutional LSTM approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp.254-266.
42. Nikhil, U.V., Pandiyan, A.M., Raja, S.P. and Stamenkovic, Z., 2024. Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models. Computers, 13(6), p.137.
43. Nischitha, K., Vishwakarma, D., Ashwini, M.N. and Manjuraju, M.R., 2020. Crop prediction using machine learning approaches. International Journal of Engineering Research & Technology (IJERT), 9(08), pp.23-26.
44. Nyéki, A. and Neményi, M., 2022.Crop yield prediction in precision agriculture. Agronomy, 12(10), p.2460.
45. Pandith, V., Kour, H., Singh, S., Manhas, J. and Sharma, V., 2020.Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis. Journal of scientific research, 64(2), pp.394-398.
46. Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C. and Athanasiadis, I.N., 2021.Machine learning for large-scale crop yield forecasting. Agricultural Systems, 187, p.103016.
47. Prasad, N.R., Patel, N.R., Danodia, A. and Manjunath, K.R., 2022. Comparative performance of semi-empirical based remote sensing and crop simulation model for cotton yield prediction. Modeling Earth Systems and Environment, 8(2), pp.1733-1747.
48. Priyatikanto, R., Lu, Y., Dash, J. and Sheffield, J., 2023. Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation. Agricultural and Forest Meteorology, 341, p.109652.
49. Qiao, M., He, X., Cheng, X., Li, P., Luo, H., Tian, Z. and Guo, H., 2021. Exploiting hierarchical features for crop yield prediction based on 3-d convolutional neural networks and multikernelgaussian process. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.4476-4489.
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2. Abdel-salam, M., Kumar, N. and Mahajan, S., 2024. A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning. Neural Computing and Applications, 36(33), pp.20723-20750.
3. Abouaomar, A., El Hanjri, M., Kobbane, A., Laouiti, A. and Nafil, K., 2025. Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems. International Conference on Wireless Networks and Mobile Communications
4. Aldossary, M., Alharbi, H.A. and Hassan, C.A.U., 2024. Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture. IEEE Access.
5. Alebele, Y., Wang, W., Yu, W., Zhang, X., Yao, X., Tian, Y., Zhu, Y., Cao, W. and Cheng, T., 2021. Estimation of crop yield from combined optical and SAR imagery using Gaussian kernel regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.10520-10534.
6. Ali, N., Mohammed, A., Bais, A., Berraies, S., Ruan, Y., Cuthbert, R.D. and Sangha, J.S., 2024.Field Scale Precision: Predicting Grain Yield of Diverse Wheat Breeding Lines Using High-Throughput UAV Multispectral Imaging. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
7. Ansarifar, J., Wang, L. and Archontoulis, S.V., 2021. An interaction regression model for crop yield prediction. Scientific reports, 11(1), pp.1-14.
8. Arshad, J., Aziz, M., Al-Huqail, A.A., Zaman, M.H.U., Husnain, M., Rehman, A.U. and Shafiq, M., 2022. Implementation of a LoRaWAN based smart agriculture decision support system for optimum crop yield. Sustainability, 14(2), p.827.
9. Ayalew, A.T. and Lohani, T.K., 2023. Prediction of Crop Yield by Support Vector Machine Coupled with Deep Learning Algorithm Procedures in Lower Kulfo Watershed of Ethiopia. Journal of Engineering, 2023(1), p.6675523.
10. Badshah, A., Alkazemi, B.Y., Din, F., Zamli, K.Z. and Haris, M., 2024. Crop Classification and Yield Prediction using Robust Machine Learning Models for Agricultural Sustainability. IEEE Access.
11. Bregaglio, S., Ginaldi, F., Raparelli, E., Fila, G. and Bajocco, S., 2023.Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets. Agricultural Systems, 209, p.103666.
12. Chen, D., Yang, P., Chen, I.R., Ha, D.S. and Cho, J.H., 2024.SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms. arXiv preprint arXiv:2402.10280.
13. Desloires, J., Ienco, D. and Botrel, A., 2023. Out-of-year corn yield prediction at field-scale using Sentinel-2 satellite imagery and machine learning methods. Computers and Electronics in Agriculture, 209, p.107807.
14. Dey, T., Bera, S., Paul, B., De, D., Mukherjee, A. and Buyya, R., 2024. Fly: Femtolet-based edge-cloud framework for crop yield prediction using bidirectional long short-term memory. Software: Practice and Experience.
15. Elavarasan, D. and Vincent, P.D., 2020. Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access, 8, pp.86886-86901
16. Elbasi, E., Zaki, C., Topcu, A.E., Abdelbaki, W., Zreikat, A.I., Cina, E., Shdefat, A. and Saker, L., 2023. Crop prediction model using machine learning algorithms. Applied Sciences, 13(16), p.9288.
17. Fei, S., Hassan, M.A., Xiao, Y., Su, X., Chen, Z., Cheng, Q., Duan, F., Chen, R. and Ma, Y., 2023. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision agriculture, 24(1), pp.187-212.
18. Gharakhanlou, N.M. and Perez, L., 2024. From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change. Science of The Total Environment, 951, p.175764.
19. Guo, Y., Xiao, Y., Hao, F., Zhang, X., Chen, J., de Beurs, K., He, Y. and Fu, Y.H., 2023. Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 124, p.103528.
20. Gupta, A. and Nahar, P., 2023. Classification and yield prediction in smart agriculture system using IoT. Journal of Ambient Intelligence and Humanized Computing, 14(8), pp.10235-10244.
21. Gupta, S., Geetha, A., Sankaran, K.S., Zamani, A.S., Ritonga, M., Raj, R., Ray, S. and Mohammed, H.S., 2022. Machine Learning-and Feature Selection-Enabled Framework for Accurate Crop Yield Prediction. Journal of Food Quality, 2022(1), p.6293985.
22. Haider, S.T., Ge, W., Li, J., Rehman, S.U., Imran, A., Sharaf, M. and Haider, S.M., 2024. An Ensemble Machine Learning Framework for Cotton Crop Yield Prediction Using Weather Parameters: A Case Study of Pakistan. IEEE Access.
23. Hiremani, V. and Devadas, V., 2025. Federated learning for crop yield prediction: A comprehensive review of techniques and applications. Journal of Agriculture and Food Research.
24. Hoque, M.J., Islam, M.S., Uddin, J., Samad, M.A., De Abajo, B.S., Vargas, D.L.R. and Ashraf, I., 2024. Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning. IEEE Access.
25. Hu, T., Zhang, X., Bohrer, G., Liu, Y., Zhou, Y., Martin, J., Li, Y. and Zhao, K., 2023. Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield. Agricultural and Forest Meteorology, 336, p.109458.
26. Idoje, G., Dagiuklas, T. and Iqbal, M., 2023. Federated Learning: Crop classification in a smart farm decentralised network. Smart Agricultural Technology, 5, p.100277.
27. Ilyas, Q.M., Ahmad, M. and Mehmood, A., 2023.Automated estimation of crop yield using artificial intelligence and remote sensing technologies. Bioengineering, 10(2), p.125.
28. Iniyan, S. and Jebakumar, R., 2022. Mutual information feature selection (MIFS) based crop yield prediction on corn and soybean crops using multilayer stacked ensemble regression (MSER). Wireless Personal Communications, 126(3), pp.1935-1964.
29. Islam, M.R., Oliullah, K., Kabir, M.M., Alom, M. and Mridha, M.F., 2023. Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation. Journal of Agriculture and Food Research, 14, p.100880.
30. Jaliyagoda, N., Lokuge, S., Gunathilake, P.M.P.C., Amaratunga, K.S.P., Weerakkody, W.A.P., Bandaranayake, P.C. and Bandaranayake, A.U., 2023. Internet of things (IoT) for smart agriculture: Assembling and assessment of a low-cost IoT system for polytunnels. Plos one, 18(5), p.e0278440.
31. Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., &Uwamahoro, A. (2023). Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize. Agriculture.
32. Li, A., Markovic, M., Edwards, P. and Leontidis, G., 2024. Model pruning enables localized and efficient federated learning for yield forecasting and data sharing. Expert Systems with Applications, 242, p.122847.
33. Li, L., Li, J., Chen, D., Pu, L., Yao, H. and Huang, Y., 2025. VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture. arXiv preprint arXiv:2504.13365.
34. Li, L., Li, J., Chen, D., Pu, L., Yao, H. and Huang, Y., 2025. FedReplay: A Feature Replay Assisted Federated Transfer Learning Framework for Efficient and Privacy-Preserving Smart Agriculture. arXiv preprint arXiv:2511.00269.
35. Mahesh, P. and Soundrapandiyan, R., 2024. Yield prediction for crops by gradient-based algorithms. Plos one, 19(8), p.e0291928.
36. Mamatha, V. and Kavitha, J.C., 2023. Machine learning based crop growth management in greenhouse environment using hydroponics farming techniques. Measurement: Sensors, 25, p.100665.
37. MirhoseiniNejad, S.M., Abbasi-Moghadam, D. and Sharifi, A., 2024.ConvLSTM-ViT: A Deep Neural Network for Crop Yield Prediction Using Earth Observations and Remotely Sensed Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
38. Mitra, A., Beegum, S., Fleisher, D., Reddy, V.R., Sun, W., Ray, C., Timlin, D. and Malakar, A., 2024. Cotton yield prediction: a machine learning approach with field and synthetic data. IEEE Access.
39. Morales, A. and Villalobos, F.J., 2023.Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science, 14, p.1128388.
40. Mughal, F.R., He, J., Das, B., Dharejo, F.A., Zhu, N., Khan, S.B. and Alzahrani, S., 2024. Adaptive federated learning for resource-constrained IoT devices through edge othersintelligence and multi-edge clustering. Scientific Reports, 14(1), p.28746.others Mukherjee, A. and Buyya, R., 2024. Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application. arXiv preprint arXiv:2408.02998.
41. Nejad, S.M.M., Abbasi-Moghadam, D., Sharifi, A., Farmonov, N., Amankulova, K. and Lászlź, M., 2022.Multispectral crop yield prediction using 3D-convolutional neural networks and attention convolutional LSTM approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp.254-266.
42. Nikhil, U.V., Pandiyan, A.M., Raja, S.P. and Stamenkovic, Z., 2024. Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models. Computers, 13(6), p.137.
43. Nischitha, K., Vishwakarma, D., Ashwini, M.N. and Manjuraju, M.R., 2020. Crop prediction using machine learning approaches. International Journal of Engineering Research & Technology (IJERT), 9(08), pp.23-26.
44. Nyéki, A. and Neményi, M., 2022.Crop yield prediction in precision agriculture. Agronomy, 12(10), p.2460.
45. Pandith, V., Kour, H., Singh, S., Manhas, J. and Sharma, V., 2020.Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis. Journal of scientific research, 64(2), pp.394-398.
46. Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C. and Athanasiadis, I.N., 2021.Machine learning for large-scale crop yield forecasting. Agricultural Systems, 187, p.103016.
47. Prasad, N.R., Patel, N.R., Danodia, A. and Manjunath, K.R., 2022. Comparative performance of semi-empirical based remote sensing and crop simulation model for cotton yield prediction. Modeling Earth Systems and Environment, 8(2), pp.1733-1747.
48. Priyatikanto, R., Lu, Y., Dash, J. and Sheffield, J., 2023. Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation. Agricultural and Forest Meteorology, 341, p.109652.
49. Qiao, M., He, X., Cheng, X., Li, P., Luo, H., Tian, Z. and Guo, H., 2021. Exploiting hierarchical features for crop yield prediction based on 3-d convolutional neural networks and multikernelgaussian process. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.4476-4489.
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51. Raja, S.P., Sawicka, B., Stamenkovic, Z. and Mariammal, G., 2022.Crop prediction based on characteristics of the agricultural environment using various feature selection techniques and classifiers. IEEE Access, 10, pp.23625-23641.
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