The product employs FasterNet given that the spine community to reinforce computational efficiency and decrease model complexity. It substitutes for the closest-neighbor upsampling with CARAFE to boost modest focus on detection capabilities. Also, the SE focus mechanism is integrated to improve leaf aspect extraction, thus maximizing recognition accuracy in complex winery environments. The experimental effects demonstrate that CSF-YOLO achieves a mAP of ninety.fifteen%, which is one.eighty two% better when compared to the baseline product, YOLOv8n. Also, the design’s inference benefits may be accessed by way of mobile equipment, demonstrating the feasibility of serious-time winery pest checking. This study provides a good technological Basis for advancing intelligent pest monitoring units in vineyards and the development of clever agriculture. Whole report (This article belongs to the Segment Precision and Digital Agriculture)
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