A study on plant disease and pest detection and counting based on multi-scale enhancement and cross-scale fusion.

This paper targets the practical needs of plant disease and pest object detection and counting in complex field environments and proposes a lightweight improved framework based on YOLOv10. A MEMBA-F multi-scale feature enhancement module is introduced on the Neck to strengthen representations of small targets and weak-texture lesions, and a CSCAF cross-scale context-aware fusion module is designed to adaptively align high-level semantics with low-level details via cross-scale attention and gated selection, suppress background interference, and improve localization stability. The proposed method is systematically compared with two-stage detectors, YOLO-series models, and Transformer-based detectors on three public datasets, and is further investigated through ablation studies, confusion matrix analysis, and Grad-CAM interpretability analysis. In addition, a density-binned counting evaluation is conducted to validate robustness from sparse to dense scenarios. Experimental results demonstrate that the proposed method achieves superior performance in Precision, Recall, mAP@50, and mAP@50-95, and significantly reduces counting errors in dense scenes under deployable inference cost, providing reliable support for precision plant protection monitoring and decision making.