Research on correlation analysis and prediction model of agricultural climate factors based on machine learning

Abstract

This article uses machine learning technology to analyze the correlation of climate factors that affect crop yields, and conduct prediction and comprehensive evaluation to guide agricultural production. This paper selects early rice crops in Guangxi as the research object. Based on the climatic data of early rice planting areas in Guangxi from 1990 to 2017, a cart decision tree is constructed to generate a random forest model to analyze the correlation between early rice yield and climatic factors in each growth period, and obtain the various growth periods The ranking of the importance of climatic factors on the yield, thus forming the basis for calculating the weights of the climatic factors in each growth period of early rice; based on the climatic data in Guilin, Guangxi from 2008 to April to July 2017, predicted by the long and short-term memory network Guilin's various climate data from April to July 2018.