Integrating morphological and deep learning approaches for the identification of economically important nematode genera in vineyards: Mesocriconema and Xiphinema.

. These two major ectoparasitic nematodes cause significant damage to grapevine root systems. Among the models tested, YOLOv11 achieved the highest detection accuracy, with a precision of 95.7 % and an mAP@50 of 93.2 %. YOLO-NAS exhibited comparable performance (mAP@50 = 92.7 %, precision = 93.1 %, recall = 84.9 %), while Roboflow 3.0 (YOLOv8 architecture) yielded satisfactory results (mAP@50 = 89.4 %), indicating its applicability for real-time diagnostic workflows. This integration of taxonomic expertise with deep learning represents a new methodological framework for nematode identification. All models exhibited rapid convergence and stable learning dynamics during training. The findings underscore the potential of YOLO-based frameworks as efficient, scalable, and reproducible tools that complement classical morphological and molecular identification, contributing to precision agriculture and sustainable nematode management strategies.