The product employs FasterNet given that the spine network to enhance computational efficiency and lessen design complexity. It substitutes for the nearest-neighbor upsampling with CARAFE to enhance tiny goal detection capabilities. In addition, the SE interest mechanism is built-in to improve leaf aspect extraction, thereby boosting recognition precision in complex vineyard environments. The experimental final results exhibit that CSF-YOLO achieves a mAP of 90.15%, which is one.eighty two% greater in comparison to the baseline model, YOLOv8n. Also, the product’s inference final results is often accessed via cellular gadgets, demonstrating the feasibility of serious-time winery pest monitoring. This study delivers a reliable specialized Basis for advancing intelligent pest checking programs in vineyards and the event of sensible agriculture. Full article (This short article belongs to the Portion Precision and Electronic Agriculture)
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