Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Improving the Recognition of Known and Unknown Plant Disease Classes Using Deep Learning

  • Yao Meng (Department of Electronics Engineering, Jeonbuk National University) ;
  • Jaehwan Lee (Department of Electronics Engineering, Jeonbuk National University) ;
  • Alvaro Fuentes (Department of Electronics Engineering, Jeonbuk National University) ;
  • Mun Haeng Lee (Department of Smart Farm, Chungnam State University) ;
  • Taehyun Kim (National Institute of Agricultural Sciences) ;
  • Sook Yoon (Department of Computer Engineering, Mokpo National University) ;
  • Dong Sun Park (Department of Electronics Engineering, Jeonbuk National University)
  • Received : 2024.06.24
  • Accepted : 2024.07.18
  • Published : 2024.08.30
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Abstract

Recently, there has been a growing emphasis on identifying both known and unknown diseases in plant disease recognition. In this task, a model trained only on images of known classes is required to classify an input image into either one of the known classes or into an unknown class. Consequently, the capability to recognize unknown diseases is critical for model deployment. To enhance this capability, we are considering three factors. Firstly, we propose a new logits-based scoring function for unknown scores. Secondly, initial experiments indicate that a compact feature space is crucial for the effectiveness of logits-based methods, leading us to employ the AM-Softmax loss instead of Cross-entropy loss during training. Thirdly, drawing inspiration from the efficacy of transfer learning, we utilize a large plant-relevant dataset, PlantCLEF2022, for pre-training a model. The experimental results suggest that our method outperforms current algorithms. Specifically, our method achieved a performance of 97.90 CSA, 91.77 AUROC, and 90.63 OSCR with the ResNet50 model and a performance of 98.28 CSA, 92.05 AUROC, and 91.12 OSCR with the ConvNext base model. We believe that our study will contribute to the community.

Keywords

Acknowledgement

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry(IPET) and Korea Smart Farm R&D Foundation(KosFarm) through Smart Farm Innovation Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA) and Ministry of Science and ICT(MSIT), Rural Development Administration(RDA)(421005-04).

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