Attention-based multi-task method for recognition of tomato leaf disease
To address the challenges of low accuracy and difficulty in handling complex environments in tomato leaf disease recognition using traditional methods,this paper proposed an Attention-based Multi-task Tomato Leaf Disease Recognition Method(AMTDR).Firstly,ResNet18 was adopted as the backbone network,with Convolutional Block Attention(CBA)modules introduced after each residual block.Secondly,a multi-task structure was designed with disease recognition and disease severity branches.The disease recognition branch accurately identified the type of tomato leaf disease,while the disease severity branch assessed the severity of the disease.In each branch,Convolutional Triplet Attention(CTA)modules were introduced to enhance the representation capability of disease features.Experimental results demonstrated that the proposed AMTDR method achieved an accuracy and F1 score of 98.54%on a dataset containing 11 types of tomato diseases in complex environments.Compared with the ResNet50 network,the accuracy and F1-score were improved by 1.27%and 1.25%,respectively,while the parameter count and FLOPs were only 48.72%and 44.30%of ResNet50.The AMTDR method effectively identified tomato leaf diseases in complex environments,providing significant value for agricultural disease recognition.
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