The method of soil moisture prediction based on hyperspectral imaging and two-stage attention mechanism

Deep learning is rapidly developing, and it is broadly utilized in prediction and data analysis due to its advantages such as powerful learning ability and computational power. The combination of deep learning and hyperspectral imaging technology can effectively improve the prediction accuracy of soil entropy. Aiming at the current problem of low accuracy of farmland soil moisture prediction, the study proposes the method of soil moisture prediction based on hyperspectral imaging and two-stage attention mechanism. The model was subjected to performance comparison analysis, and it was found that the MAE and RMSE of the model were 0.019 and 0.031, respectively. This result indicated that the prediction accuracy and precision of the farmland soil moisture prediction model proposed by the study were better than those of the comparison models. Subsequently, the model was analyzed for its application effects, and it was found that the model's prediction curves of farmland soil moisture for the four regions differed very little from the real value curves, and the fit was very high. The above results show the effectiveness and practicality of the farmland soil entropy sentiment prediction model based on LSTM and two-stage attention proposed in the study. It helps to improve the accuracy of soil entropy prediction in high agricultural fields and can provide a theoretical basis for improving the quality and growth of crops.