Decision Tree and Knowledge Graph Based on Grain Loss Prediction

Abstract

China is an agricultural country. Agricultural production is an import part of the Chinese economic system. With the advent of the information age, plenty of data have been produced in a series of links about harvest and after-harvest, such as harvest, processing, transportation, and consumption. With proper use of these data, we can dig out more and more valuable information from the data. In this paper, the relevant algorithm of machine learning is adopted and improved to predict the grain loss after extracting the data of harvesting link. Machine learning is the core of Artificial Intelligence, and its application covers all fields. In this paper, based on the traditional machine learning algorithm—decision tree, the knowledge graph is used to make appropriate improvements to predict the grain-loss after harvest.