An explainable Bi-LSTM model for winter wheat yield prediction.

Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. Although other sequential DL methods like 1D Convolutional Neural Networks (1D-CNN) and Bidirectional long short-term memory (Bi-LSTM) have shown high accuracy for various tasks, including crop yield prediction, their application in regional scale crop yield prediction remains largely unexplored.