Open AccessPrediction of Multiple Diseases of Soybean in Complex Environment Based on Improved Apriori Algorithm
Author(s) -
Xiaonan Hu,
Fangyi Deng,
Yu Zeng,
Yan Guo,
Yanqing Fang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/5/052117
Subject(s) - apriori algorithm , a priori and a posteriori , computer science , algorithm , disease , big data , data mining , machine learning , association rule learning , medicine , philosophy , epistemology , pathology
The existence and persistence of soybean diseases are not conducive to the effective operation of the global soybean market. Many detection and prediction methods have been used to prevent and detect soybean diseases, but the practicability of these methods has always been a big challenge for researchers due to there are too few variables in the prediction model, which show bad prediction effect of soybean disease in complex environment. In this paper, the popular Apriori algorithm in data mining is used to analyze the common disease data of soybean in complex environment, so as to achieve the goal of early prediction and control of soybean disease. The variables used in this paper are the characteristic factors of 35 kinds of Soybean under 18 common diseases. The experimental results show that the improved Apriori algorithm can complete the better prediction of soybean diseases in complex environment, so as to reduce the impact of diseases on soybean yield, which is of great significance for economic development, agricultural production and other fields.