Research on prediction model of quantitative seed supply of super rice

In view of the phenomenon of seed blocking and uneven supply during the operation of quantitative seed feeding device, Xiuyou 5 super rice was taken as the research object to study the seed supply theory and seed yield prediction. Using Python as the algorithm framework, BP neural network, decision tree and XGboost algorithm model were used to predict the performance of vibrating rice sowing device. In order to verify the validity of the model, combined with the data of the last 14 times of the test set, the determination coefficient R~2 and relative error were used as evaluation indexes to check the prediction accuracy of each model, and the optimal seed supply prediction model was obtained by comparative analysis. The results showed that the R~2 of BP neural network model was 0.87 and the relative error was 18%, the R~2 of decision tree model was 0.91 and the relative error was 11%, and the R~2 of XGboost model was 0.95 and the relative error was 5%. Compared with the other two models, XGboost model predicted the seed supply with higher degree of fit and more significant prediction effect. It can provide the basis for determining the working parameters of the quantitative seed feeder and facilitate the producers to make scientific decisions.