Abstract: The storage loss of grain during post-harvest stages is a major problem that plagues grain storage enterprises, and thus also it is an important factor affecting the economic benefits of enterprises. Therefore, the assessment of the storage loss of grain is of great significance for post-harvest loss reduction of grain. This paper investigates the factors influencing storage loss of grain through questionnaires, and models the data by the Support Vector Machine (SVM) model to intelligently evaluate the grain loss in storage stage. Meanwhile, in order to improve the accuracy of the model, this paper uses Random Drift Particle Swarm Optimization (RDPSO) algorithm to train the parameters of SVM, by making full use of the strong global search ability of the RDPSO algorithm to find the optimal solution of the model parameters. The experimental results show that the SVM model optimized by RDPSO algorithm can obtain more accurate grain loss prediction than basic SVM model and linear regression model.

Key words: storage stage, loss of grain, support vector machine, random drift particle swarm