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 achieve high-precision detection of four major cassava leaf diseases in complex unstructured environments, an improved algorithm for cassava leaf disease neural network detection based on the selective attention mechanism, MAISNet, was proposed. Using V2-ResNet-101 as the base network, the multiattention algorithm was firstly used to optimize the weighting coefficients, adjust the semantic expression of the feature channels, and the semantic feature saliency expression of cassava leaf disease in the feature map was preliminary constructed; then the instance batch normalization method was used after the residual unit to suppress the covariate offset in the feature expression, highlight the target semantic feature expression in the feature map, and realize the high-quality semantic feature expression. Finally, the Squareplus activation function was used to replace the ReLU activation function in the residual branch to maintain the numerical distribution of semantic features in the negative domain, and reduce the truncation errors in the feature fitting process. The results of the comparison test showed that the MAISNet-101 neural network constructed after the above improvement achieved an average accuracy of 95.39% for the detection of four common cassava leaf diseases, which was significantly better than the performance of the mainstream algorithms such as EfficientNet-B5 and RepVGG-B3g4. The results of the visualization and analysis of the extracted features of the network showed that high-quality semantic feature saliency representation of cassava leaf diseases was the key to improve the accuracy of cassava leaf disease detection. The proposed MAISNet neural network model can accomplish high-precision detection of cassava leaf diseases in real scenarios, which can provide technical support for precise drug application.