Prediction analysis of cotton yield based on BP neural network and multiple linear regression

Abstract

With the rapid development of the economy and agricultural production, cotton is an indispensable crop in people's production and life, and the average annual cotton yield is particularly significant in production. The unit yield of cotton is affected by many factors, so it is extremely important to analyze the factors affecting future cotton yield forecast. This article varies seven relevant indicators, including fertilizer cost,consumption of agricultural plastic, pesticide cost, seed cost, irrigation cost, chlorpyrifos control area, and labor input, and establishes a prediction model based on Back Propagation (BP) neural network. The model uses the function approximation ability of BP neural network and gradient descent algorithm to predict the nonlinear functional relationship between the unit output and the seven factors. Meanwhile, a multiple linear regression model is used to forecast the unit yield and the prediction accuracy of the two methods for unit output is compared. The experimental results show that compared with multiple linear regression, BP neural network has higher accuracy in predicting cotton unit yield, which can be used as an idea for future prediction.