Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2025.05a
  • /
  • Pages.687-690
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  • 2025
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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PLDC-80: A Combined Augmented Hybrid Plant Leaf Disease Classification Benchmarking Dataset

PLDC-80: 결합 증강형 하이브리드 식물 잎 질병 분류 벤치마킹 데이터 세트

  • David J. Richter (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Kyungbaek Kim (Dept. of Artificial Intelligence Convergence, Chonnam National University)
  • ;
  • 김경백 (전남대학교, 인공지능융합학과)
  • Published : 2025.05.29
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Abstract

Plant leaf disease identification is essential to ensure that plant leaf diseases can be identified early, kept from spreading too far, and as a result it is essential to ensure less crop and therefore food waste during production. To train deep learning models capable of assisting workers in the process, datasets that are diverse, large, and high quality are needed but creating them is laborious. Fields, staff, equipment, hardware and experts are needed and make the creation even more difficult and not everyone has access to the means to create them. However, a true benchmark dataset in hybrid conditions would greatly help the field, so in this paper we introduce a methodology to combine, augment, balance, etc. open datasets to create a new dataset with over 300,000 images across 80 classes called PLDC-80. The dataset methods are available at: https://github.com/JDatPNW/PLDC-80

Keywords

Acknowledgement

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry(IPET) through the Agriculture and Food Convergence Technologies Program for Research Manpower development, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA)(project no. RS-2024-00397026, 34%). This work was supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156287, 33%). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629, 33%) grant funded by the Korea government(MSIT).

References

  1. M. Xu, H. Kim, J. Yang, A. Fuentes, Y. Meng, S. Yoon, T. Kim, and D. S. Park, "Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning", Frontiers in Plant Science, vol. 14, p. 1225409, 2023.
  2. J. Lu, L. Tan, and H. Jiang, "Review on convolutional neural network (cnn) applied to plant leaf disease classification", Agriculture, vol. 11, no. 8, p. 707, 2021.
  3. M. Xu, J.-E. Park, J. Lee, J. Yang, and S. Yoon, "Plant disease recognition datasets in the age of deep learning: challenges and opportunities", Frontiers in Plant Science, vol. 15, p. 1452551, 2024.
  4. D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, "Plantdoc: A dataset for visual plant disease detection", in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. New York, NY, USA: Association for Computing Machinery, 2020, pp. 249-253.
  5. S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using deep learning for image-based plant disease detection", Frontiers in plant science, vol. 7, p. 1419, 2016.
  6. X. Liu, W. Min, S. Mei, L. Wang, and S. Jiang, "Plant disease recognition: A large-scale benchmark dataset and a visual region and loss reweighting approach", IEEE Transactions on Image Processing, vol. 30, pp. 2003–2015, 2021.
  7. A. Ahmad, D. Saraswat, A. E. Gamal, and G. Johal, "Cd&s dataset: Handheld imagery dataset acquired under field conditions for corn disease identification and severity estimation", arXiv preprint arXiv:2110.12084, 2021.
  8. R. Thapa, K. Zhang, N. Snavely, S. Belongie, and A. Khan, "The plant pathology challenge 2020 data set to classify foliar disease of apples", Applications in plant sciences, vol. 8, no. 9, p. e11390, 2020.
  9. Petchiammal, B. Kiruba, Murugan, and P. Arjunan, "Paddy doctor: A visual image dataset for automated paddy disease classification and benchmarking", in Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD), 2023, pp. 203-207.
  10. S. Thite, Y. Suryawanshi, K. Patil, and P. Chumchu, "Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications", Data in Brief, vol. 53, p. 110268, 2024.
  11. E. Mwebaze, T. Gebru, A. Frome, S. Nsumba, and J. Tusubira, "icassava 2019 fine-grained visual categorization challenge", arXiv preprint arXiv:1908.02900, 2019.
  12. U. Afzaal, B. Bhattarai, Y. R. Pandeya, and J. Lee, "An instance segmentation model for strawberry diseases based on mask r-cnn", Sensors, vol. 19, p. 6565, 2021.
  13. G. Fenu and F. M. Malloci, "Diamos plant: A dataset for diagnosis and monitoring plant disease", Agronomy, vol. 11, no. 11, p. 2107, 2021.
  14. D. J. Richter and K. Kim, "Assessing the performance of domainspecific models for plant leaf disease classification: A comprehensive benchmark of transfer-learning on open datasets", Research Square preprint, 2024.
  15. A. Ahmad, "CD&S Dataset", 2021, accessed: 2024-10-21. [Online]. Available: https://osf.io/s6ru5/
  16. R. Thapa, K. Zhang, N. Snavely, S. Belongie, and A. Khan, "Plant pathology 2021 - fgvc8", Kaggle, 2021, accessed: 2024-10-21. [Online]. Available: https: //kaggle.com/competitions/plant-pathology-2021-fgvc8
  17. S. P. Mohanty, "Plantvillage dataset", https://github.com/spMohanty/PlantVillage-Dataset, 2016, accessed: 2024-10- 21.
  18. Petchiammal and P. Arjunan, "Paddy disease classification", https://kaggle.com/competitions/ paddy-disease-classification, 2022, kaggle.
  19. S. Thite, Y. Suryawanshi, K. PATIL, and prawit chumchu, "Sugarcane leaf image dataset", 2023, accessed: 2024-10-21. [Online]. Available: https://doi.org/10.17632/9twjtv92vk.1
  20. E. Mwebaze, J. Mostipak, Joyce, J. Elliott, and S. Dane, "Cassava leaf disease classification", 2020, accessed: 2024-10-21. [Online]. Available: https://kaggle.com/ competitions/cassava-leaf-disease-classification
  21. X. Liu, W. Min, S. Mei, L. Wang, and S. Jiang, "Pdd271: Plant disease recognition dataset", https://github.com/ liuxindazz/PDD271, 2020, gitHub Repository.
  22. U. Afzaal, "Strawberry disease detection dataset", 2021, accessed: October 23, 2024. [Online]. Available: https://www.kaggle.com/datasets/usmanafzaal/ strawberry-diseasedetection-dataset
  23. G. Fenu and F. M. Malloci, "Diamos plant dataset: A dataset for diagnosis and monitoring plant disease", 2021, accessed: 2024-10-21. [Online]. Available: https://zenodo.org/records/5557313
  24. M. I. Bappi, D. J. Richter, and K. Kim, "Assessing the effectiveness of augmentation techniques in enhancing plant leaf disease classification", Smart Media Journal, vol. 14, no. 1, pp. 17–25, 2025.
  25. A. L. A. Haikal, N. Yudistira, and A. Ridok, "Comprehensive mixedbased data augmentation for detection of rice leaf disease in the wild", Crop Protection, p. 106816, 2024.
  26. D. J. Richter, "PLDC80", 2025, accessed: 2025-03-27, https://github.com/JDatPNW/PLDC-80/