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.