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Plant Leaf Disease Detection Using Adaptive Neuro-Fuzzy Classification

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 943))

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Abstract

The paper deals with classification of different types of diseases of tomato and brinjal/eggplant. The patterns of the diseases are considered as a feature. It may be possible that the diseases are recognized by its texture patterns. A method that uses the texture patterns of the diseases in pure grayscale is applied for feature extraction purpose. A dedicated GLCM matrix is used to compute the features. The ANFIS based classification model is used for disease recognition by classification. The pattern based features with ANFIS recognition gives accuracy of 90.7% and 98.0% for TPDS 1.0 and BPDS 1.0 datasets respectively.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Applications, DAV University Jalandhar, Punjab, 144012, India

    Hiteshwari Sabrol

  2. Department of Computer Science and Applications, P.U. SSG Regional, Hoshiarpur, 146023, Punjab, India

    Satish Kumar

Authors
  1. Hiteshwari Sabrol
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  2. Satish Kumar
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Corresponding author

Correspondence to Hiteshwari Sabrol .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Sabrol, H., Kumar, S. (2020). Plant Leaf Disease Detection Using Adaptive Neuro-Fuzzy Classification. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_32

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