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Leaf disease recognition based on channel information attention network

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Abstract

Aiming at the problem of the variety of plant leaf diseases and how to extract effective features, an attention network model fused with channel information is proposed to identify a variety of plant leaf diseases. Firstly, a residual structure based basic network is built for feature extraction, and in order to extract effective information, the feature is re-calibrated by integrating multiple channel information through the attention network. Then, the constraint information is added into the cross entropy function to accelerate the convergence of the model. Finally, the model is tested on the data sets of 16 diseases of four different plants. The results show that the recognition accuracy of the basic network model is 83.13%, while the accuracy increased by 4.64% after fusing the channel information network. Compared with other models, the fusion model improves the recognition accuracy by 9.72% and the model complexity is less than twice that of the optimal model in the comparison experiment.

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Availability of data and materials

The data sets supporting the results of this article are included within the article.

Code availability

Correspondence and requests for code should be addressed to H.D.

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Acknowledgments

This study was supported by research grants from the National Natural Science Foundation of China(61873178,61976150), Key Research and Development Projects of Shanxi Province(201803D31038), Key R & D projects in Jinzhong City, China grant number Y192006, the Central Guidance on Local Science and Technology Development Fund of Shanxi. Shanxi Provincial Central Guide local Science and technology development fund project (YDZJSX2021C005,YDZJSX2022A016). The open project of CAD & CG State Key Laboratory of Zhejiang University in 2022 (A2221).

Funding

This study was supported by research grants from the National Natural Science Foundation of China(61873178,61976150), Key Research and Development Projects of Shanxi Province(201803D31038), Key R & D projects in Jinzhong City, China grant number Y192006, the Central Guidance on Local Science and Technology Development Fund of Shanxi. Shanxi Provincial Central Guide local Science and technology development fund project (YDZJSX2021C005,YDZJSX2022A016). The open project of CAD & CG State Key Laboratory of Zhejiang University in 2022 (A2221).

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    Authors and Affiliations

    1. Department of Information and Computer, Taiyuan University of Technology, No.209, University Street, Jinzhon, 030600, China

      Hongxia Deng, Dongsheng Luo, Jinxiu Hou, Guanyu Qian & Haifang Li

    2. Department of Software, Taiyuan University of Technology, No.209, University Street, Jinzhon, 030600, China

      Zijing Zhou

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    Contributions

    All authors wrote and reviewed the manuscript text. H.D.conceived the experiment, implemented and tested the proposed model. Z.Z. and J.H. designed the disease recognition experiments, wrote all programs. J.H.and D.L. prepared all data and figures, performed the disease recognition, and drafted the manuscript. G.Q.analysed the results. H.L. contributed to analyses and discussion.

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    Correspondence to Hongxia Deng.

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    Dongsheng Luo, Zijing Zhou, Jinxiu Hou, Guanyu Qian and Haifang Li contributed equally to this work.

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    Deng, H., Luo, D., Zhou, Z. et al. Leaf disease recognition based on channel information attention network. Multimed Tools Appl 83, 6601–6619 (2024). https://doi.org/10.1007/s11042-023-15512-9

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