Areca palm yellow leaf disease (YLD) detection and severity grading from UAV Imagery with an enhanced YOLOv10s model

Efficient and accurate assessment of the severity of areca palm (Areca catechu L.) yellow leaf disease (YLD) is essential for early prevention, precision pesticide application, and the sector's long-term viability. However, accurate detection of YLD from UAV imagery remains a significant challenge owing to severe canopy occlusion, subtle early-stage lesions, intricate background clutter, and minimal inter-class variance. Given these challenges, we introduce an optimized YOLOv10s model for YLD detection. First, a non-parametric SimAM attention mechanism is incorporated into the backbone to engineer the C2f_SimAM module; this design optimizes feature extraction within lesion areas while successfully mitigating intricate background noise. Second, the Histogram Transformer Block (HTB) is embedded into the deep feature extraction stage, which strengthens multi-scale feature extraction via dynamic-range histogram self-attention, thereby advancing the network's capability in capturing diminutive targets. Finally, we incorporate a Soft-NMS-GIoU strategy to alleviate localization bias and reduce missed detections under dense occlusion. The upgraded YOLOv10s outperforms the baseline by 2.4 percentage points, reaching 92.9% mAP0.5. Precision and recall reach 88.7% and 86.8%, respectively, at a frame rate of 88.350 FPS. The confusion between healthy and mild YLD categories is significantly reduced, with their respective recall rates improved by 3% and 7%. Field deployment in areca palm plantations demonstrates the model's proficiency in pinpointing disease hotspots and generating visualization maps, confirming its practicality and reliability. The methodology developed in this work offers a robust technical foundation for the large-scale automated monitoring and precise management of YLD.