Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.10a
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  • Pages.640-643
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  • 2024
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Using MobileNet to Predict Unseen Corn Diseases

MobileNet을 사용한 학습되지 않은 옥수수 질병 예측

  • 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 : 2024.10.31
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Abstract

Agriculture and the plants and crops that are the results of it are essential to our everyday lives. Without agriculture our current society can not function and as such it is extremely important to make sure that we can assure that farms and farmers can continuously and steadily harvest enough produce. One big challenge that keep hindering the farming process are plant diseases that account for a large number of dead crops. They spread fast and can be troublesome to detect, especially manually. Datasets are also sparce and often lacking. To ease the detection process and speed it up, in an effort to aid farmers, we propose a pretrained MobileNet CNN deep learning AI model that can automatically detect maize/corn diseases from images. However, since data is rather sparce, not all diseases can be accounted for. This is why we have trained the model with one set of diseases (leaf spot and leaf blight) and healthy plant leaves, but tested it with a set of images of maize leaves that are infected with a disease the model has never seen before (leaf rust). The model has managed to not only learn and master the test set, but also managed to generalize to the before unseen rust infected leaf images. This promises to help future models learn robust and effective models that can generalize to diseases that can even classify diseases new to the model, which can be important in a field with limited data.

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). 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) grant funded by the Korea government(MSIT)

References

  1. T. B. Shahi, C.-Y. Xu, A. Neupane, and W. Guo, "Recent advances in crop disease detection using uav and deep learning techniques," Remote Sensing, vol. 15, no. 9, p. 2450, 2023.
  2. E. Moupojou, A. Tagne, F. Retraint, A. Tadonkemwa, D. Wilfried, H. Tapamo, and M. Nkenlifack, "Fieldplant: A dataset of field plant images for plant disease detection and classification with deep learning," IEEE Access, vol. 11, pp. 35398-35410, 2023.
  3. D. P. Hughes and M. Salathe, "An open access repository of images on plant health to enable the development of mobile disease diagnostics," arXiv e-prints, pp. arXiv-1511, 2015.
  4. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861, 2017.
  5. A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, et al., "Searching for mobilenetv3," in Proceedings of the IEEE/CVF international conference on computer vision, pp. 1314-1324, 2019.
  6. F. Mohameth, C. Bingcai, and K. A. Sada, "Plant disease detection with deep learning and feature extraction using plant village," Journal of Computer and Communications, vol. 8, no. 6, pp. 10-22, 2020.
  7. V. Pandey, U. Tripathi, V. K. Singh, Y. S. Gaur, and D. Gupta, "Survey of accuracy prediction on the Plantvillage dataset using different ml techniques," EAI Endorsed Transactions on Internet of Things, vol. 10, 2024.
  8. D. Mamba Kabala, A. Hafiane, L. Bobelin, and R. Canals, "Image-based crop disease detection with federated learning," Scientific Reports, vol. 13, no. 1, p. 19220, 2023.