Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Novel Category Discovery in Plant Species and Disease Identification through Knowledge Distillation

  • Jiuqing Dong (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 : 2024.06.21
  • Accepted : 2024.07.11
  • Published : 2024.07.31
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Abstract

Identifying plant species and diseases is crucial for maintaining biodiversity and achieving optimal crop yields, making it a topic of significant practical importance. Recent studies have extended plant disease recognition from traditional closed-set scenarios to open-set environments, where the goal is to reject samples that do not belong to known categories. However, in open-world tasks, it is essential not only to define unknown samples as "unknown" but also to classify them further. This task assumes that images and labels of known categories are available and that samples of unknown categories can be accessed. The model classifies unknown samples by learning the prior knowledge of known categories. To the best of our knowledge, there is no existing research on this topic in plant-related recognition tasks. To address this gap, this paper utilizes knowledge distillation to model the category space relationships between known and unknown categories. Specifically, we identify similarities between different species or diseases. By leveraging a fine-tuned model on known categories, we generate pseudo-labels for unknown categories. Additionally, we enhance the baseline method's performance by using a larger pre-trained model, dino-v2. We evaluate the effectiveness of our method on the large plant specimen dataset Herbarium 19 and the disease dataset Plant Village. Notably, our method outperforms the baseline by 1% to 20% in terms of accuracy for novel category classification. We believe this 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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