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A Comparative Analysis on AI Techniques for Grape Leaf Disease Recognition

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1377))

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Abstract

Grape or Grapevine (Vitis Vinifera) belonging to the Vitaceae family is one of India’s most commercially important fruit crops. It is a widely temperate crop that has become accustomed to the subtropical climate of the peninsula of India. In a Grape vineyard, there are more chances of grape fruits and its leaf to confront with diseases. Manual Observation is not feasible and is also time constrained for experts and agronomists to track with. Inorder to predict the disease in early stage, we deal with a literature survey on different methods pertinent to disease identification and classification. Through this survey, we find out the best method to be followed for disease tracking. The composition at first, presents a detailed terminology related to different kind of grape leaf diseases. Furthermore, a survey regarding automated grape leaf disease identification and categorization methods are carried out, which deals with techniques like machine learning and deep learning.

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Authors and Affiliations

  1. Vimal Jyothi Engineering College, Kannur, Kerala, India

    Swetha Pai & Manoj V. Thomas

Authors
  1. Swetha Pai
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  2. Manoj V. Thomas
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Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Pai, S., Thomas, M.V. (2021). A Comparative Analysis on AI Techniques for Grape Leaf Disease Recognition. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_1

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  • DOI: https://doi.org/10.1007/978-981-16-1092-9_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1091-2

  • Online ISBN: 978-981-16-1092-9

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