Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks
AbstractAmong 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. View Full-Text
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Kim, Y.; Roh, J.-H.; Kim, H.Y. Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks. Sustainability 2018, 10, 34.
Kim Y, Roh J-H, Kim HY. Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks. Sustainability. 2018; 10(1):34.Chicago/Turabian Style
Kim, Yangseon; Roh, Jae-Hwan; Kim, Ha Y. 2018. "Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks." Sustainability 10, no. 1: 34.
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