Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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A Hierarchical Deep Convolutional Neural Network for Crop Species and Diseases Classification

Deep Convolutional Neural Network(DCNN)을 이용한 계층적 농작물의 종류와 질병 분류 기법

  • Borin, Min (School of Computer Science, Chungbuk National University) ;
  • Rah, HyungChul (Research Institute of Veterinry Medicine, Chungbuk National University) ;
  • Yoo, Kwan-Hee (School of Computer Science, Chungbuk National University)
  • Received : 2022.08.07
  • Accepted : 2022.10.31
  • Published : 2022.11.30
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Abstract

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.

Keywords

Acknowledgement

This research was supported by the "Cooperative Research Program for Agriculture Science and Technology Development" of the Rural Development Administration, Republic of Korea (Project No. PJ015341012022) and by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2022-2020-0-01462) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)".

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