Hybrid attention and learning mechanism-based deep learning model for crop yield prediction

Abstract An accurate crop yield prediction is necessary for augmenting farming practices and guaranteeing the security of food. Several approaches have been developed to predict the yield of crops. The conventional approaches were incapable of discovering non-linear relationship amid the data, and they exhibit several drawbacks, such as computational overhead, false errors, overfitting issues, limited resources, poor generalizability and computational complexity, which lead to inaccurate predictions. To overcome these limitations, the research proposed a Multi-Attention and Distilled Task Learning enabled Weighted Decision tree based Bidirectional Long Short-Term Memory (MA-DTL-WDBiLSTM) model to predict the crop yield accurately. The model’s performance in forecasting the crop yield is enhanced due to the incorporation of multi attention mechanism and Distilled Task Learning, which distilled the gained knowledge from the teacher model to the student model and split the tasks into subtasks, respectively. Additionally, the integration of the weighted decision tree in the Bi-LSTM model offers an improved prediction accuracy by handling the intricate data and concentrating on the relevant features. The model is evaluated using the Crop yield prediction dataset and the Agricultural Crop Yield in Indian States Dataset, which enhances the interpretability and better generalizability. Specifically, the proposed MA-DTL-WDBiLSTM model achieved a minimum correlation of 0.884, MAE of 1.759, MSE of 6.536, and RMSE of 2.556 using the Agricultural Crop Yield in Indian States Dataset, showcasing high performance in crop yield prediction and outperforming other existing models.