Optimizing Disease Identification in Mango Leaves: A Channel Attention Approach with Enhanced EfficientNet

Miah, Parvez; Saha, Chamak; Saha, Somak; Hossain, Muhammad Iqbal

doi:10.1109/iceeict62016.2024.10534486

2024 6th International Conference on Electrical Engineering and Information &Amp;amp; Communication Technology (ICEEICT) 2024

DOI: 10.1109/iceeict62016.2024.10534486

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Optimizing Disease Identification in Mango Leaves: A Channel Attention Approach with Enhanced EfficientNet

Parvez Miah,

Chamak Saha,

Somak Saha

et al.

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2024

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Cited by 1 publication

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A classification and recognition model for multiple fruit tree leaf diseases

Du,

Li,

Qin

2024

Environ. Res. Commun.

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Fruit tree leaf diseases affect both fruit survival rate and orchard revenue. Rapid and accurate identification of tree leaf diseases not only prevents the spread of diseases but also ensures healthy tree growth and improves fruit quality. Existing models mostly focus on individual types of tree leaf diseases, with limited applications in the identification of multiple tree leaf diseases. In response to the insensitivity of current classification models to disease region features and the issue of increased error rates due to the presence of similar diseases, this study proposes a residual network model based on the ECA channel attention mechanism and meta-Acon adaptive activation function.The model employs ResNet34 as the backbone network and incorporates the ECA module after each residual block to focus on relevant information. Additionally, a new activation function called meta-Acon is introduced to enhance the model's generalization ability through its dynamic learning capability. Finally, the model's recognition performance is improved by fusing bottom-level features with features from other layers using the Feature Pyramid Network (FPN).Experimental results on a dataset augmented with Mosaic processing show that the FPEM-ResNet34 model achieves a classification accuracy of 98.46%. Compared to other common models such as VGG-16, Inception-V1,AlexNet, and ResNet50, the proposed method mentioned in this paper demonstrates more effective improvement in the accuracy of tree leaf classification, making it highly valuable for practical applications..

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A classification and recognition model for multiple fruit tree leaf diseases

Du,

Li,

Qin

2024

Environ. Res. Commun.

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show abstract

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Optimizing Disease Identification in Mango Leaves: A Channel Attention Approach with Enhanced EfficientNet

Cited by 1 publication

References 16 publications

A classification and recognition model for multiple fruit tree leaf diseases

A classification and recognition model for multiple fruit tree leaf diseases

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