Soil moisture prediction model based on LSTM and Elman neural network

Abstract

China is a large agricultural country, and in the process of agricultural production, it is very important to make accurate prediction of soil moisture. To address the problems of local minimization and slow convergence of traditional BP (back propagation) neural network in the prediction process, this paper combines LSTM (long short-term memory) and Elman neural network with traditional BP neural network model, and proposes a method based on LSTM and Elman neural network for soil moisture prediction. A soil moisture prediction method based on LSTM and Elman neural network is proposed. The prediction model of LSTM and Elman neural network was developed, and the soil moisture of Xilinguole grassland in Inner Mongolia was predicted and experimented. The results show that the accuracy of the model is higher than that of the unoptimized BP neural network. The model is able to reduce the use of moisture sensors significantly, which reduces the cost for agricultural production.