In order to cope with Chinese cabbage diseases that are prone to occur in the heart area,accurate identifica-tion of Chinese cabbage heart location is of key significance to achieve precise drug application.To address this prob-lem,an improved YOLOv8 algorithm with the introduction of an attention mechanism is proposed,aiming to improve the model's ability to identify the heart region of Chinese cabbage and achieve real-time detection.Three mainstream atten-tion mechanisms are selected for comparison experiments,and the results show that CBAM is the most significant in terms of model performance enhancement,with the improved model reaching 87.6%mean average precision(mAP50),86.3%precision and90.0%recall,and the processing speed is maintained at 26 frames per second.Detection tests confirmed that the model could accurately identify targets in complex backgrounds.The research results are expected to significantly reduce pesticide use,reduce environmental burden,and enhance the efficiency and sustainability of agricul-tural production.
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