An end-to-end model for rice yield prediction using deep learning fusion

Rice yield is essential for more than half of the world's population, and thus, accurate predictions of rice yield are of great importance for trade, development policies, humanitarian assistance, decision-makers, etc. However, traditional mechanistic models and statistical machine learning models need to identify features, making the research on and application of these models laborious and time-consuming. In this paper, a novel end-to-end prediction model that fuses two back-propagation neural networks (BPNNs) with an independently recurrent neural network (IndRNN), named BBI-model, is proposed to address these challenges. In stage one, BBI-model preprocesses the original area and meteorology data. In stage two, one BPNN and the IndRNN are used to learn deep spatial and temporal features in parallel. In stage three, another BPNN combines two kinds of deep features and learns the relationships between these deep features and rice yields to make predictions for summer and winter rice yields. The experimental results indicate that BBI-model achieved the lowest mean absolute error (MAE) and root mean square error (RMSE) for the summer rice prediction (0.0044 and 0.0057, respectively) and corresponding values of 0.0074 and 0.0192 for the winter rice prediction when the number of layers in the network was set to six. Moreover, the errors of the model using the combination of deep spatial-temporal features were significantly lower than when simply using deep temporal features. Furthermore, the model converged quickly with 100 iterations and then remained stable. These findings confirm that the model can make accurate predictions for summer and winter rice yields of 81 counties in the Guangxi Zhuang Autonomous Region, China.