Abstract
Supervised learning will be a bottleneck for developing plant disease identification since it relies on learning from massive amounts of carefully labeled images, which is costly and time-consuming. On the contrary, self-supervised learning has succeeded in various image classification tasks; however, it has not been applied broadly in the plant disease analysis process. This work, therefore, studies the effectiveness of self-supervised learning using contrastive pre-training with SimCLR for plant disease image classification. We investigated unsupervised pre-training scenarios on unlabeled plant images across multiple architectures, including supervised fine-tuning on labeled samples. In addition, we explored the label efficiency of the self-supervised approach, acquired by fine-tuning the models on various fractions of labeled images. Our results demonstrated that the performance of self-supervised learning on plant disease became comparable to that of the supervised training approach.
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Data availability
The datasets generated during and/or analyzed during the current study are courtesy of Bhattarai (2023) and available in the kaggle repository of https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset.
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Bunyang, S., Thedwichienchai, N., Pintong, K. et al. Self-supervised learning advanced plant disease image classification with SimCLR. Adv. in Comp. Int. 3, 18 (2023). https://doi.org/10.1007/s43674-023-00065-z
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DOI: https://doi.org/10.1007/s43674-023-00065-z