TY  - JOUR 
AU  - Hoque, Jiabul
AU  - Islam, Saiful
AU  - Uddin, Jia
AU  - Samad, Abdus
AU  - Sainz de Abajo, Beatriz
AU  - Ramírez Vargas, Débora Libertad
AU  - Ashraf, Imran
PY  - 2024
SN  - 2169-3536
UR  - https://uvadoc.uva.es/handle/10324/67046
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...
LA  - eng
PB  - IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC.
KW  - Deep learning models
KW  - Agriculture
KW  - Crop yield prediction
KW  - Machine Learning
KW  - Deep learning
TI  - Incorporating meteorological data and pesticide information to forecast crop yields using machine learning
DO  - 10.1109/ACCESS.2024.3383309
ER  -