基于改进Densenet的烟草叶片病害识别方法OA

[Background]This study aims to achieve real-time and accurate identification of tobacco leaf diseases using an improved DenseNet model,thereby ensuring the quality and yield of tobacco leaves.[Methods]A high-precision classification method for tobacco leaf diseases was developed using an improved DenseNet model based on multi-scale feature fusion and an attention mechanism.The SE-MSFF-Deep Block,consisting of SEBlock and multi-scale feature fusion,was employed to extract multi-scale features.A Vision Transformer(ViT)-style attention structure was added before the classification layer to integrate scattered disease feature regions.The Focal Loss function was introduced to enhance the recognition accuracy of hard-to-classify and minority samples.Transfer learning was applied to mitigate overfitting on small datasets.[Results]The SMVF-DenseNet model achieved an accuracy of 97.58％for tobacco leaf diseases with an FPS of 23.69.Compared to other mainstream models,it significantly reduced inter-class confusion for difficult-to-class and minority samples.Moreover,the SMVF-DenseNet model maintained competitive classification accuracy on cross-crop plant disease datasets,demonstrating strong generalization capabilities.[Conclusion]The tobacco leaf disease recognition method based on multi-scale feature fusion and the attention mechanism effectively captures both detailed and overall features of disease patches,improving classification accuracy for difficult categories.Validation on additional plant disease datasets shows that the method excels in tobacco disease recognition and demonstrates strong cross-crop generalization,providing a feasible solution for broadly applicable plant disease identification models.