A prediction model for grain yield in Henan province based on BP neural network

Abstract

Henan Province is an important agricultural province in China, and its food production is crucial for meeting the country's food needs and ensuring food security. This article establishes a prediction model for grain yield in Henan Province based on BP neural network. Six indicators are selected as input variables, including total power of agricultural machinery, effective irrigation area, converted amount of agricultural fertilizer application, pesticide usage, sowing area of grain crops, and rural electricity consumption. Grain yield is used as output variable. The experimental results show that the error rate of the BP neural network prediction model in the training and validation stages is controlled within 3%, indicating that the model has good prediction performance and is helpful for the government to formulate agricultural planning and agricultural production management strategies.