September 5, 2025ElectronicsOpen Access

A Unified Self-Supervised Framework for Plant Disease Detection on Laboratory and In-Field Images

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This framework enhances plant disease detection accuracy in images, highlighting improved performance with minimal labeled data.

Achieved an accuracy of 77.82% in plant disease detection using self-supervised learning methods.
The model maintained macro-averaged precision, recall, and F1-score of 80.00%, 78.24%, and 77.48% respectively.
Utilized a hybrid loss function integrated with data augmentation techniques to enhance generalization.
Pretrained model fine-tuned on the PlantVillage dataset achieved a high accuracy of 99.85%, showcasing strong adaptability.

Huan et al. (2025) studied this question.

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