The design employs FasterNet because the spine network to improve computational effectiveness and cut down model complexity. It substitutes for the nearest-neighbor upsampling with CARAFE to improve tiny concentrate on detection abilities. Additionally, the SE focus mechanism is built-in to enhance leaf function extraction, thereby maximizing recognition precision in complicated vineyard environments. The experimental outcomes display that CSF-YOLO achieves a mAP of 90.15%, which is 1.82% greater when compared to the baseline product, YOLOv8n. In addition, the product’s inference results is usually accessed by using mobile devices, demonstrating the feasibility of true-time winery pest monitoring. This research delivers a sound technological foundation for advancing clever pest monitoring methods in vineyards and the development of sensible agriculture. Whole report (This short article belongs to the Segment Precision and Electronic Agriculture)
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