Abstract
Precise prediction of irrigation volumes is crucial in modern agriculture. This study proposes an optimized long short-term memory (LSTM) model-based irrigation prediction method that combines bidirectional LSTM networks. The model provides farmers with more precise irrigation management decisions, facilitating optimal utilization of water resources and effective crop production management. This proposed model aims to fully exploit spatio-temporal features and sequence dependencies to enhance prediction accuracy and reliability. We aim to fully leverage crop irrigation volumes' spatio-temporal features and sequence dependencies to improve prediction accuracy and reliability. First, this study adopts a bidirectional LSTM (BiLSTM) model to simulate the temporal features of irrigation volumes and learn the sequential dependencies of crop growth data from historical records. Then, this study passes the irrigation volume data through a convolutional neural network (CNN) model to extract spatial features and capture correlations among various features such as temperature, precipitation, and wind speed. Our prediction performance significantly improved after incorporating an attention mechanism that involves weighting features and enhancing focus on crucial aspects. The proposed BiLSTM-CNN-Attention approach is used to predict irrigation volume for spring corn in significant irrigation areas in Jilin Province, China. The results demonstrate that the proposed method surpasses recurrent neural network (RNN), CNN, LSTM, BiLSTM, and BiLSTM-CNN methods in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) (0.000004, 0.005968, 0.004599), and R(2) (0.9749), making a superior solution for predicting the volume of crop irrigation.