Identification of apple leaf disease via novel attention mechanism based convolutional neural network

Abstract

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IntroductionThe identification of apple leaf diseases is crucial for apple production.MethodsTo assist farmers in promptly recognizing leaf diseases in apple trees, we propose a novel attention mechanism. Building upon this mechanism and MobileNet v3, we introduce a new deep learning network.Results and discussionApplying this network to our carefully curated dataset, we achieved an impressive accuracy of 98.7% in identifying apple leaf diseases, surpassing similar models such as EfficientNet-B0, ResNet-34, and DenseNet-121. Furthermore, the precision, recall, and f1-score of our model also outperform these models, while maintaining the advantages of fewer parameters and less computational consumption of the MobileNet network. Therefore, our model has the potential in other similar application scenarios and has broad prospects.

Keywords

• apple leaf disease
• classification
• deep learning
• attention mechanism
• multi-scale feature extraction