Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks

Yangseon Kim, Jae-Hwan Roh and Ha Young Kim
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Yangseon Kim: Crop Cultivation & Environment Research Division, National Institute of Crop Science, Suwon 441-853, Korea
Jae-Hwan Roh: Crop Cultivation & Environment Research Division, National Institute of Crop Science, Suwon 441-853, Korea
Ha Young Kim: Department of Financial Engineering, Ajou University, Worldcupro 206, Yeongtong-gu, Suwon 16499, Korea

Sustainability, 2017, vol. 10, issue 1, 1-20

Abstract: Among all diseases affecting rice production, rice blast disease has the greatest impact. Thus, monitoring and precise prediction of the occurrence of this disease are important; early prediction of the disease would be especially helpful for prevention. Here, we propose an artificial-intelligence-based model for rice blast disease prediction. Historical data on rice blast occurrence in representative areas of rice production in South Korea and historical climatic data are used to develop a region-specific model for three different regions: Cheolwon, Icheon and Milyang. A rice blast incidence is then predicted a year in advance using long-term memory networks (LSTMs). The predictive performance of the proposed LSTM model is evaluated by varying the input variables (i.e., rice blast disease scores, air temperature, relative humidity and sunshine hours). The most widely cultivated rice varieties are also selected and the prediction results for those varieties are analyzed. Application of the LSTM model to the accumulated rice-blast disease score data confirms successful prediction of rice blast incidence. In all regions, the predictions are most accurate when all four input variables are combined. Rice blast fungus prediction using the proposed LSTM model is variety-based; therefore, this model will be more helpful for rice breeders and rice blast researchers than conventional rice blast prediction models.

Keywords: artificial intelligence; machine learning; deep learning; rice blast; early prediction; long short-term memory; recurrent neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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