Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition - Inria - Institut national de recherche en sciences et technologies du numérique
Article Dans Une Revue Neurocomputing Année : 2024

Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition

Résumé

Deep learning models have demonstrated great promise in plant disease identification. However, existing approaches often face challenges when dealing with unseen crop-disease pairs, limiting their practicality in real-world settings. This research addresses the gap between known and unknown (unseen) plant disease identification. Our study pioneers the exploration of the zero-shot setting within this domain, offering a new perspective to conceptualizing plant disease identification. Specifically, we introduce the novel Cross Learning Vision Transformer (CL-ViT) model, incorporating self-supervised learning, in contrast to the previous state-of-the-art, FF-ViT, which emphasizes conceptual feature disentanglement with a synthetic feature generation framework. Through comprehensive analyses, we demonstrate that our novel model outperforms state-of-the-art models in both accuracy performance and visualization analysis. This study establishes a new benchmark and marks a significant advancement in the field of plant disease identification, paving the way for more robust and efficient plant disease identification systems. The code is available at https://github.com/abelchai/Cross-Learning-Vision-Transformer-CL-ViT.
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hal-04815172 , version 1 (02-12-2024)

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Abel Yu Hao Chai, Sue Han Lee, Fei Siang Tay, Pierre Bonnet, Alexis Joly. Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition. Neurocomputing, 2024, 610, pp.128608. ⟨10.1016/j.neucom.2024.128608⟩. ⟨hal-04815172⟩
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