Early forecasting of rice blast disease using long short-term memory recurrent neural networks

Yangseon Kim, Jae Hwan Roh, Ha Young Kim

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


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.

Original languageEnglish
Article number34
JournalSustainability (Switzerland)
Issue number1
Publication statusPublished - 2018 Jan 1

Bibliographical note

Funding Information:
Acknowledgments: We thank the National Institute of Crop Science for providing data. This research was supported by a grant (17CTAP-C129782-01) from the Technology Advancement Research Program funded by the Korean Ministry of Land, Infrastructure and Transport.

Publisher Copyright:
© 2017 by the author.

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


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