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Computer Vision Method Applied for Detecting Diseases in Grape Leaf System

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Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 810))

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Abstract

Grape diseases detection is an important issue in sustainable agriculture. Recognition method of grape leaf diseases is put forward based on computer vision. A computer vision detection system is constructed to acquire the grape leaf disease images. The grape leaf disease regions are segmented by Otsu method, and the morphological algorithms are used to improve the lesion shape. Prewitt operator is selected to extract the complete edge of lesion region. Grape leaf diseases recognition model based on back–propagation (BP) neural network can efficiently inspect and recognize five grape leaf diseases: leaf spot, Sphaceloma ampelinum de Bary, anthracnose, round spot, and downy mildew. The results indicate that the proposed grape leaf diseases detection system can be used to inspect grape diseases with high classification accuracy.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (No. U1304305); Scientific Research Tackling Key Subject of Henan Province (No. 142102310550, No. 162102110122, No. 172102210300, No. 182102110116); Natural Science Foundation of Henan Province (No. 142300410419); Science and Technology Research Project of Zhengzhou (No. 121PPTGG465–2).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, China

    Ang Wu, Juanhua Zhu & Yujing He

Authors
  1. Ang Wu
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  2. Juanhua Zhu
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  3. Yujing He
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Correspondence to Juanhua Zhu .

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Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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Wu, A., Zhu, J., He, Y. (2020). Computer Vision Method Applied for Detecting Diseases in Grape Leaf System. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_36

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