Research in Plant Disease (식물병연구)

The Korean Society of Plant Pathology (한국식물병리학회)
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Artificial Intelligence Plant Doctor: Plant Disease Diagnosis Using GPT4-vision

  • Yoeguang Hue (Department of Plant Medicine, Sunchon National University) ;
  • Jea Hyeoung Kim (Department of Plant Medicine, Sunchon National University) ;
  • Gang Lee (Department of Computer Engineering, Sunchon National University) ;
  • Byungheon Choi (Department of Multimedia Engineering, Sunchon National University) ;
  • Hyun Sim (Department of Industry Academic Collaboration Foundation, Sunchon National University) ;
  • Jongbum Jeon (Korea Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB)) ;
  • Mun-Il Ahn (EPINET Co., Ltd.) ;
  • Yong Kyu Han (EPINET Co., Ltd.) ;
  • Ki-Tae Kim (Department of Plant Medicine, Sunchon National University)
  • Received : 2024.02.05
  • Accepted : 2024.03.06
  • Published : 2024.03.31
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Abstract

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

Keywords

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation) in 2024 (2023-0-00028).

References

  1. Farber, C., Mahnke, M., Sanchez, L. and Kurouski, D. 2019. Advanced spectroscopic techniques for plant disease diagnostics. A review. TrAC, Trends Anal. Chem. 118: 43-49.
  2. Huang, H., Zheng, O., Wang, D., Yin, J., Wang, Z., Ding, S. et al. 2023. ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model. Int. J. Oral Sci. 15: 29.
  3. Jackulin, C. and Murugavalli, S. 2022. A comprehensive review on detection of plant disease using machine learning and deep learning approaches. Meas.: Sens. 24: 100441.
  4. Kim, S.-H., Choi, J., Choi, Y.-J., Park, B.-Y., Lee, S.-H., Kim, G. H. et al. 2023. Introduction of list of plant diseases in Korea 6.1st edition (2023 revised version). Res. Plant Dis. 29: 331-344. (In Korean)
  5. Martin, R. R., James, D. and Levesque, C. A. 2000. Impacts of molecular diagnostic technologies on plant disease management. Annu. Rev. Phytopathol. 38: 207-239. https://doi.org/10.1146/annurev.phyto.38.1.207
  6. Matta, C. 2010. Spontaneous generation and disease causation: Anton de Bary's experiments with Phytophthora infestans and late blight of potato. J. Hist. Biol. 43: 459-491. https://doi.org/10.1007/s10739-009-9220-1
  7. Qing, J., Deng, X., Lan, Y. and Li, Z. 2023. GPT-aided diagnosis on agricultural image based on a new light YOLOPC. Comput. Electron. Agric. 213: 108168.
  8. Riu, M., Son, J.-S., Oh, S.-K. and Ryu, C.-M. 2022. Aromatic agriculture: volatile compound-based plant disease diagnosis and crop protection. Res. Plant Dis. 28: 1-18. (In Korean) https://doi.org/10.5423/RPD.2022.28.1.1
  9. Song, S., Chung, H., Kim, K.-H. and Kim, K.-T. 2022. Analysis of rice blast outbreaks in Korea through text mining. Res. Plant Dis. 28: 113-121. (In Korean) https://doi.org/10.5423/RPD.2022.28.3.113
  10. Wang, D. Q., Feng, L. Y., Ye, J. G., Zou, J. G. and Zheng, Y. F. 2023. Accelerating the integration of ChatGPT and other large-scale AI models into biomedical research and healthcare. MedComm Future Med. 2: e43.