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

• Published in: Frontiers in plant science Vol. 14; p. 1274231
• Main Authors: Cheng, Hebin, Li, Heming
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 18.10.2023
• Subjects: apple leaf disease; attention mechanism; classification; deep learning; multi-scale feature extraction; Plant Science
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2023.1274231

Introduction The identification of apple leaf diseases is crucial for apple production. Methods To 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 discussion Applying 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.