Depth-based segment any leaf: A zero-shot pipeline for plant disease detection
Engineering Applications of Artificial Intelligence, cilt.180, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 180
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.engappai.2026.115242
- Dergi Adı: Engineering Applications of Artificial Intelligence
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
- Anahtar Kelimeler: Background removal, Classification, Generalization, Plant disease detection, Segmentation, Zero-shot evaluation
- Ankara Üniversitesi Adresli: Evet
Özet
Accurate leaf segmentation plays a crucial role in plant phenotyping and classification tasks, as it directly influences the reliability of downstream analyses. To effectively identify target leaves in images containing distracting background elements, this study presents an integrated end-to-end fine-tuning free data processing pipeline. The proposed pipeline consists of several stages, including depth map generation, depth-aware segmentation, filtering of distant objects based on depth information, merging overlapping masks, and selecting the mask closest to the center of the input image. The resulting segmented leaf regions are subsequently used for plant disease classification with widely adopted deep learning architectures. Experimental results on in-domain test datasets indicate that the overall classification performance remains relatively stable with and without the application of the pipeline. However, under zero-shot evaluation settings, where the models are tested on previously unseen datasets, the proposed pipeline provides substantial improvements in generalization capability. Additional feature-space and distribution-level analyses further demonstrate that the proposed pipeline reduces dataset-specific bias and improves feature alignment across domains. Overall, the proposed pipeline enables accurate and efficient extraction of leaf regions even in visually complex environments, while simultaneously acting as an implicit feature regularization mechanism.