Densenet based on coordinated attention for cotton aphid disease severity classification

Wenxia Bao; Yuanyuan Wang; Dongyan Zhang; Hongbo Qiao; Dong Liang

doi:10.1117/12.2652533

20 October 2022 Densenet based on coordinated attention for cotton aphid disease severity classification

Wenxia Bao, Yuanyuan Wang, Dongyan Zhang, Hongbo Qiao, Dong Liang

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123500X (2022) https://doi.org/10.1117/12.2652533
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

The classification of cotton aphid disease severity is conducive to the accurate control of cotton aphids. Because the cotton aphid images taken in the natural environment have some problems, such as complex background, different lighting conditions and difficult to distinguish between different grades, the existing convolutional neural networks do not have high accuracy in the classification of cotton aphid grades in the natural scene images. This study proposes an improved DenseNet classification network, CA_DenseNet_BC_100. It is used to classify the severity of cotton aphids in natural scene images. CA_DenseNet_BC_100 network inherits the advantages of DenseNet_BC_100 network design and applies a novel attention coordination module. The experimental results show that the classification accuracy of CA_DenseNet_BC_100 network for the severity of cotton aphids in natural scene images is better than that of a series of existing networks such as resnet50, ShuffleNetv2, GhostNet, MobileNetv3 and DenseNet.

Citation Download Citation

Wenxia Bao, Yuanyuan Wang, Dongyan Zhang, Hongbo Qiao, and Dong Liang "Densenet based on coordinated attention for cotton aphid disease severity classification", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123500X (20 October 2022); https://doi.org/10.1117/12.2652533

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KEYWORDS

Image classification

Network architectures

Performance modeling

Agriculture

Convolutional neural networks

Data modeling

Scene classification

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