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Mobile convolution neural network for the recognition of potato leaf disease images

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Abstract

Plant disease management plays a crucial role in food security as diverse diseases can cause a substantial reduction of crop yield. As an important staple, potatoes are highly consumed over the entire world, while they are easy to be infected by various diseases too. The early recognition and warning can suppress the outbreak of potato diseases and increase the yield of crops. For this purpose, the paper proposes a novel network architecture named MobOca_Net to recognize potato diseases. The lightweight MobileNet V2 was chosen as the foundation network, and to improve the learning capability of minute crop lesion features, we modified the classical MobileNet-V2 by incorporating the attention mechanism behind the pre-trained network, which was followed by an octave convolution block for extracting high-dimensional features. Moreover, the transfer learning from the PlantVillage dataset was applied for model training. The proposed procedure delivered a superior performance gain over other compared methods, and it realized an average identification accuracy of 97.73% on different potato disease types. Experimental findings present a competitive performance and prove the validity of the proposed procedure.

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Acknowledgments

The authors would like to thank Fundamental Research Funds for the Central Universities (No. 20720181004) and the authors also thank editors and anonymous reviewers for providing constructive advice.

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Authors and Affiliations

  1. Department of Information and Electrical Engineering, Ningde Normal University, Ningde, 352100, China

    Weirong Chen

  2. School of Informatics, Xiamen University, Xiamen, 361005, China

    Junde Chen, Adnan Zeb, Shuangyuan Yang & Defu Zhang

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  1. Weirong Chen
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  2. Junde Chen
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  3. Adnan Zeb
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  4. Shuangyuan Yang
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  5. Defu Zhang
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Correspondence to Shuangyuan Yang.

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Chen, W., Chen, J., Zeb, A. et al. Mobile convolution neural network for the recognition of potato leaf disease images. Multimed Tools Appl 81, 20797–20816 (2022). https://doi.org/10.1007/s11042-022-12620-w

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  • DOI: https://doi.org/10.1007/s11042-022-12620-w

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