Addressing the low recognition rate of small strawberry targets in complex backgrounds,this study proposed an improved YOLOv8n model to enhance the accuracy of strawberry target recognition.In the experimental process,the SPD-Conv module was incorporated into the model structure to improve the model's ability to handle small objects and low-resolution images,thereby increasing robustness in complex scenes.The PSA attention mechanism proposed by YOLOv10 was then integrated to embed global representation learning capability at a low computational cost,further enhancing model performance.Lastly,the WIoU loss function replaced the CIoU loss function to address its limitations.Compared to the original model,the improved YOLOv8n model achieved 0.9％increase in precision and 4.3％increase in recall.Additionally,mAP50 and mAP50-95 was improved by 3％and 3.5％,respectively.The improved YOLOv8n model significantly enhanced the accuracy of strawberry target detection and demonstrated superior detection performance for small strawberry targets in complex backgrounds.
维普
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