Meng Wang1, Jinghan Cai1, Wenzheng Liu1, Xue Yang1, Jingjing Zhang1, Qiangmin Zhou1, Fanzhen Wang1, Hang Zhang1,*, Tonghai Liu2,*
Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541
- 30 January 2026
Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >