Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

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To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of in-put data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combin-ing two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast pre-diction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the perfor-mance of an ANN-based disease prediction model was improved by applying suitable machine learning tech-niques together with the optimization of hyperparame-ter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.