Application Of GA-BP Neural Network In Quality Detection And Grading Of Maize Seeds

China's agricultural development has always been the leading level in the world,and the development of agriculture has also contributed to China's economic development and social stability.In recent years,the supply of grain in our country has increased greatly with the increase of the population in our country.China's three major crops are rice,wheat and corn,corn as one of them must have an important position,because it is not only our people's main rations,but also one third of the world's main food.Maize production starts from breeding,and the first step of breeding is to select high-quality varieties.If manual sorting method is always used,not only will the quality of maize be misjudged by human factors,but also the yield of maize cannot be guaranteed.To preserve food security and improve the efficiency of agricultural production,this paper applies artificial intelligence classification model to maize breeding,which is conducive to the precision and efficiency of maize breeding,and thus to improve agricultural production efficiency.This paper,taking shandong province corn will test results as application background,the BP neural network is established corn seed quality grading model,test results indicate that there are still many shortcomings in BP algorithm.Aiming at the problem that the randomness of the initial weights and thresholds leads to the poor overall effect of the neural network,this present paper proposes an optimum proposal to optimize the initial weights and thresholds of the neural network by using genetic algorithm.In this paper,the training,simulation and performance comparison of traditional BP neural network and optimized GA-BP neural network are described in detail.From the experimental results,the accuracy of GA-BP neural network is higher,and it is more appropriate for the specific problems.