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oai:uvadoc.uva.es:10324/670462024-04-08T19:01:13Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Hoque, Jiabul Islam, Saiful Uddin, Jia Samad, Abdus Sainz de Abajo, Beatriz Ramírez Vargas, Débora Libertad Ashraf, Imran 2024-04-07T09:33:58Z 2024-04-07T09:33:58Z 2024 IEEE Access, Marzo 2024, vol. 12, p. 47768-47786. 2169-3536 https://uvadoc.uva.es/handle/10324/67046 10.1109/ACCESS.2024.3383309 47768 47786 IEEE Access 12 2169-3536 Producción Científica The agricultural sector is more vulnerable to the adverse effects of climate change and excessive pesticide application, posing a significant risk to global food security. Accurately predicting crop yields is essential for mitigating these risks and providing information for sustainable agricultural practices. This research presents a novel crop yield prediction system utilizing a year’s worth of meteorological data, pesticide records, crop yield data, and machine learning techniques. We employed rigorous methods to gather, clean, and enhance data and then trained and evaluated three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression. We utilized the GridSearchCV method for hyper-parameter tweaking to identify the most suitable hyper-parameter throughout K-Fold cross-validation, aiming to improve the model’s performance by avoiding overfitting. The remarkable performance of the Gradient Boosting model, with an almost flawless coefficient of determination (R2) of 99.99%, demonstrates its promise for precise yield prediction. This research also examined the correlation between projected and actual crop yields and identified the ideal meteorological conditions. It paves the way for data-driven methods in sustainable agriculture and resource distribution, ultimately leading to a more secure future regarding food availability and robustness to climate change. European University of Atlantic application/pdf eng IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ "© Todos los derechos reservados". Propietario de los derechos: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. Attribution-NonCommercial-NoDerivatives 4.0 Internacional Deep learning models Agriculture Crop yield prediction Machine Learning Deep learning Incorporating meteorological data and pesticide information to forecast crop yields using machine learning info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://ieeexplore.ieee.org/document/10485420 SI