Soil Nutrient Evaluation System Based on Improved Recurrent Neural Network

Soil nutrients are a comprehensive reflection of soil characteristics, and this paper proposes a recurrent neural network method with a fusion mechanism to construct a soil nutrient evaluation model in this area. Soil nutrient indicators are used as the input of the neural network, and soil nutrient levels are used as the output. The results obtained by the recurrent neural network are better through experimental simulation training and prediction, and the experimental comparison with related neural network models shows that the grade results obtained by the recurrent neural network are more accurate, and the establishment and prediction of this model provides a new way for soil nutrient fertility grade evaluation, and the MSE 0.031 of the fused recurrent neural network proposed in this paper is smaller than the mean square error value of the traditional model. The decision coefficient R2 of 0.978 is closer to 1 compared with other models, indicating that the initial weights and thresholds of the recurrent neural network optimized using the algorithm of this paper can effectively improve the prediction accuracy and fitting performance of the model.