Identification of apple leaf diseases based on MA-ConvNext network and stepwise relational knowledge distillation
The backgrounds are cluttered,the spot sizes of apple leaf disease are varying in complex environments,and the existing models have the problems of multiple parameters and a large amount of calculation.Thus,an apple leaf disease recognition network,ConvNext network based on attention and multiscale feature fusion(MA-ConvNext),was proposed.A multiscale spatial reconstruction and channel reconstruction block(MSCB)and a feature extraction block with triplet attention fusion(TAFB)were utilized to effectively extract the features at different scales and enhance the focus on leaf disease spots.Additionally,a stepwise relational knowledge distillation method was employed to fuse the"teacher"network(MA-ConvNext)with an"intermediate"network(DenseNet121)to guide the training of the"student"network(EfficientNet-B0)and achieve the model lightweighting.Experimental results showed that MA-ConvNext achieved a recognition accuracy of 99.38%,improving by 3.98 percentage points,7.55 percentage points and 4.27 percentage points compared to ResNet50,MobileNet-V3,and EfficientNet-V2 networks,respectively.After the stepwise relational knowledge distillation,the recognition accuracy further improved by 1.76 percentage points,with a smaller network size and parameters of 1.56×107 and 5.29×106.respectively.The proposed method offers new insights and technical support for the precise detection of pests and diseases in agriculture.
apple leaf disease identificationattentionmultiscale feature fusionstepwise relationshipknow-ledge distillation
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