ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

To address the challenges of detecting cotton leaf diseases in natural environments and the difficulty of manually designing feature extractors that capture similar feature expressions as those of cotton leaf diseases, an improved attention mechanism YOLO v7 algorithm (CBAM-YOLO v7) was proposed. Building upon the YOLO v7 model, the approach integrated the convolutional block attention module (CBAM) into the backbone and head of the model and incorporated a four times downsampling step within the head. The CBAM-YOLO v7 model was employed for the identification of cotton leaf diseases in Southern Xinjiang, and comparative experiments were conducted against YOLO v5 and YOLO v7. Experimental results revealed that in terms of aphid and normal leaf detection, YOLO v7 achieved favorable detection outcomes. Notably, CBAM-YOLO v7 demonstrated higher accuracy in detecting diseases like Fusarium wilt, cotton mirid bugs, and red spider mites when compared with other models. CBAM-YOLO v7 achieved a mean average precision (mAP) of 85.5%, representing a 21 percentage points increase over YOLO v5 and a 4.9 percentage points increase over YOLO v7. Moreover, the detection time for a single image was 29.26ms, offering a theoretical foundation for online monitoring of cotton leaf diseases.