The product employs FasterNet given that the spine community to improve computational performance and reduce design complexity. It substitutes for the closest-neighbor upsampling with CARAFE to improve little target detection capabilities. Furthermore, the SE focus mechanism is integrated to optimize leaf function extraction, thereby enhancing recognition accuracy in intricate vineyard environments. The experimental benefits exhibit that CSF-YOLO achieves a mAP of ninety.fifteen%, which happens to be one.82% greater compared to baseline model, YOLOv8n. On top of that, the product’s inference effects is often accessed via cell equipment, demonstrating the feasibility of authentic-time vineyard pest checking. This review supplies a stable complex foundation for advancing intelligent pest checking systems in vineyards and the event of clever agriculture. Entire article (This article belongs into the Part Precision and Electronic Agriculture)
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