PlantCLR: contrastive self-supervised pretraining for generalizable plant disease detection.

Abstract

Deep learning has improved automated plant disease detection by increasing recognition accuracy and robustness compared with traditional vision-based methods. Self-supervised learning (SSL) further reduces dependence on manual labels, but its transferability across heterogeneous agricultural datasets remains insufficiently characterized. Here, we evaluate a contrastive SSL pretraining and fine-tuning pipeline, termed PlantCLR, for plant disease classification under cross-dataset transfer with target-domain fine-tuning. PlantCLR combines SimCLR-style contrastive pretraining with a lightweight convolutional classifier to balance representation quality and deployment efficiency. Experiments on PlantVillage and Cassava Leaf Disease show strong performance, achieving 99.10% accuracy and 99.04% F1-score on PlantVillage, and 96.83% accuracy and 96.70% F1-score on Cassava. Feature embedding visualization using t-SNE and explanation maps using Grad-CAM indicate improved class separability and attention to disease-relevant regions. These results suggest that contrastive SSL can improve representation transfer while maintaining computational efficiency, supporting scalable plant disease diagnostics in practical agricultural settings. Code is available at GitHub .

Plant phenotyping relevance

植物病害を画像から分類するPlantCLR手法を開発し、異なるデータセット間で性能評価・検証しているため、植物の病害状態を推定するフェノタイピング手法が中心である。

Code and data availability

The paper's plant disease detection experiments use two publicly available image datasets with explicit Kaggle URLs in the Data availability statement. The authors also state code is available at GitHub, but no concrete URL is provided, so no code asset is included.