A grain loss prediction method based on integration of multiple classification models.

Grain production is an essential part of the Chinese economic system. It not only related to the survival and health of each people but also plays a critical role in social stability. However, more and more foods are wasted in different stages such as harvest, production, storage, and consumption. Therefore, it is essential to accurately evaluate the loss rate of grain during different stages and deal with it accordingly. With the advantages of the information technologies, a large volume of data has been collected in the different stages of grain production, such as harvest, processing, transportation, and consumption. In this paper, we propose an integrated structure to combine multiple clustering models to analyze the grain loss rate in different stages. kNN, softmax regression, decision tree, and XGBoost algorithms are studied and integrated with the proposed combined framework. The experimental results on the survey dataset suggested that the relevant algorithms of machine learning can be combined to improve the prediction accuracy of the grain loss rates. The evaluation results indicate that the proposed method can improve the accuracy to 94% in the test dataset which is higher than any other compared methods [ABSTRACT FROM AUTHOR]