RT info:eu-repo/semantics/article
T1 Incorporating meteorological data and pesticide information to forecast crop yields using machine learning
A1 Hoque, Jiabul
A1 Islam, Saiful
A1 Uddin, Jia
A1 Samad, Abdus
A1 Sainz de Abajo, Beatriz
A1 Ramírez Vargas, Débora Libertad
A1 Ashraf, Imran
K1 Deep learning models
K1 Agriculture
K1 Crop yield prediction
K1 Machine Learning
K1 Deep learning
AB 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.
PB IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC.
SN 2169-3536
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/67046
UL https://uvadoc.uva.es/handle/10324/67046
LA eng
NO IEEE Access, Marzo 2024, vol. 12, p. 47768-47786.
NO Producción Científica
DS UVaDOC
RD 22-may-2024