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THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Towards Improved Performance on Plant Disease Recognition with Symptoms Specific Annotation

  • Dong, Jiuqing (Department of Electronics Engineering, Jeonbuk National University) ;
  • Fuentes, Alvaro (Department of Electronics Engineering, Jeonbuk National University) ;
  • Yoon, Sook (Department of Computer Engineering, Mokpo National University) ;
  • Kim, Taehyun (National Institute of Agricultural Sciences) ;
  • Park, Dong Sun (Department of Electronics Engineering, Jeonbuk National University)
  • Received : 2022.05.13
  • Accepted : 2022.06.02
  • Published : 2022.05.31
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Abstract

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1A6A1A09031717); 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); and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (NRF-2021R1A2C1012174).

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