Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks

Abstract

Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable ability in many prediction scenarios. However, directly utilizing BP neural networks in soil nutrient prediction may not yield promising results due to the random assignment of initial weights and thresholds and the tendency to fall into local extreme points. In this study, a BP neural network model optimized by an improved genetic algorithm (IGA) was proposed to predict soil nutrient time series with high accuracy. First, the crossover and mutation operations of the genetic algorithm (GA) were improved. Next, the IGA was used to optimize the BP model. The symmetric nature of the model lies in its feedforward and feedback connections, i.e., the same weights must be used for the forward and backward passes. An empirical evaluation was performed using annual soil nutrient data from China. Soil pH, total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were selected as evaluation indicators. The prediction results of the IGA–BP, GA–BP, and BP neural network models were compared and analyzed. For the IGA–BP prediction model, the coefficient of determination for soil pH was 0.8, while those for total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were all greater than 0.98, exhibiting a strong generalization ability. The root-mean-square errors of the IGA–BP prediction models were reduced to 50% of the BP models. The results indicated that the IGA–BP method can accurately predict soil nutrient content for future time series.