The Plant Pathology Journal

한국식물병리학회 (The Korean Society of Plant Pathology)
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Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

  • Lee, Kyung-Tae (Department of Agricultural Biotechnology, Seoul National University) ;
  • Han, Juhyeong (Department of Agricultural Biotechnology, Seoul National University) ;
  • Kim, Kwang-Hyung (Department of Agricultural Biotechnology, Seoul National University)
  • 투고 : 2022.04.30
  • 심사 : 2022.05.29
  • 발행 : 2022.08.01
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초록

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining 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 prediction. 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 performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

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과제정보

This work was supported by the New Faculty Startup Fund from Seoul National University. The authors sincerely give thanks to Woo-il Lee, the Extension Specialist of the Rural Development Administration of Korea, for generously providing the historical data of rice blast occurrence from the National Crop Pest Management System.

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