A novel detection method based on a self-supervised framework integrating multi-scale spatial relationships for tomato leaf disease

Accurate diagnosis of tomato leaf diseases is essential to ensure optimised tomato production and reduce losses. However, their fine-grained visual features are challenging to capture and are susceptible to interference from complex agricultural environments. Conventional object detection methods rely on large-scale annotated datasets, which limit detection efficiency and accuracy. This study proposes a novel method, the Cross-layer Spatial Feature Fusion Mask Autoencoder (CSF-MAE), based on a self-supervised framework that integrates multi-scale spatial relationships to precisely detect nine distinct types of tomato leaf disease. The proposed method does not require many annotated samples. It exhaustively captures local and global image features using learnable spatial relationship modules and cross-layer feature fusion mechanisms, thereby enhancing fine-grained detection performance and environmental adaptability. Comparative experiments demonstrated that the proposed model outperformed the baseline, achieving 99.05% accuracy, 99.01% precision, 99% recall and 99% F1-score. The findings revealed that this technique displayed outstanding robustness across intricate backgrounds and high-occlusion scenes, efficiently accomplishing accurate detection of early tomato leaf diseases, with significant practical application value.