Abstract
Today machine learning and deep learning are being used in all industrial and social reform. The use and acceptability of AI solutions are rapidly growing. So, traditional agriculture practices adopt these modern technologies and move towards precision farming. Pest detection and classification is one of the critical areas of concern for agriculture and farmers. Deep learning-based detection and classification have recently automated the process, making detection significantly faster. However, centralized training is required to upload crop images (infected or not infected) which leads to privacy invasion and may lead to a negative reputation for the crop. It may incur financial losses due to the low pricing of the harvest to the farmer. Therefore, we provide privacy-preserving pest detection and classification using personalized federated learning that generates detection models based on agricultural characteristics while the farmers keep the data. We provide a performance comparison between centralized and federated approaches for five classes of pests. Further, we perform experiments to create a group-based personalized model. Through experiments, we found that the accuracy of the federated approach is lower than the centralized training. We also found that the performance of groups varies in personalized FL, so the accuracy of Group A (0.69) is higher than Group B (0.63). Based on the experimental result, the proposed solution is suitable for agriculture because of the privacy preservation and personalization with distributed and low computing devices.
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Acknowledgement
This research was supported by the MSIT Korea under the NRF Korea (NRF-2022R1A2C4001270) and the Information Technology Research Center (ITRC) support program (IITP-2022-2020-0-01602) supervised by the IITP, and the KIAT grant funded by the Korean government (MOTIE) (P0017123, The Competency Development Program for Industry Specialist).
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Yoon, J., Kumar, A., Jang, J., Kim, J., Choi, B.J. (2023). Privacy-Preserving Pest Detection Using Personalized Federated Learning. In: Saini, M.K., Goel, N., Shekhawat, H.S., Mauri, J.L., Singh, D. (eds) Agriculture-Centric Computation. ICA 2023. Communications in Computer and Information Science, vol 1866. Springer, Cham. https://doi.org/10.1007/978-3-031-43605-5_5
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