A Study of Shiitake Disease and Pest Image Analysis based on Deep Learning

딥러닝 기반 표고버섯 병해충 이미지 분석에 관한 연구

  • Jo, KyeongHo (Dept. of Multimedia Eng., School of Information Communication&Multimedia, Sunchon National University) ;
  • Jung, SeHoon (Dept. of Bigdata Convergence, School of Major Connection, Youngsan University) ;
  • Sim, ChunBo (Dept. of Multimedia Eng., School of Information Communication&Multimedia, Sunchon National University)
  • Received : 2019.11.22
  • Accepted : 2019.12.10
  • Published : 2020.01.31


The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.


Supported by : Korea Forestry Promotion Institute, National Research Foundation of Korea(NRF)


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