| Summary: | 碩士 === 國立中興大學 === 資訊科學與工程學系所 === 107 === Rice blast disease is the disease that has the greatest impact on rice growth in Taiwan. The goal of this research is to correlate historical climate data and rice blast disease data by classification models to predict if the rice blast disease will be exacerbated by a given climatic conduction. The data set used in the study was provided by the council of agriculture, executive yuan, Taiwan. The data we use are five annual climatic data (ranging from 2014 to 2018) and the field observation of rice blast disease during these years. With the data, we conduct feature selection by recursive feature elimination algorithm to analyze the key features on the rice blast disease. Through the features correlation analysis, we learn classification models by auto-sklearn and neural network. The experiment result shows that our model is with an accuracy of about 72% to correctly predict the condition (exacerbated or relived) of rice blast diseases. For the exacerbation case, our model can have 89%accuracy, demonstrating the effectiveness of the proposed classification model.
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