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Orange Leaf Diseases Identification Using Digital Image Processing

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Artificial Intelligence Algorithms and Applications (ISICA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1205))

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Abstract

A new framework is proposed to identify disease symptoms in navel oranges’ leaves using images obtained in real conditions of the field. The approach proposed consists of the following steps, the first step is leaf symptoms segmentation using Grabcut algorithm to remove background elements and thresholding to remove healthy region, then color, texture and SIFT features were extracted and used to train KNN, Random Forest and MSVM classifiers.

The approach is then tested on identification of two common diseases affecting the navel oranges of Jiangxi; Citrus Greening and Citrus Canker images, and the accuracy achieved was 91.1% on Haralick features using images in Lab color space by Random Forest and K-Neural Network classifiers.

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Authors and Affiliations

  1. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 34100, Jiangxi, China

    Irene Anney Joseph, Muhammad Asim Khan & Huilan Luo

Authors
  1. Irene Anney Joseph
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  2. Muhammad Asim Khan
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  3. Huilan Luo
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Corresponding author

Correspondence to Huilan Luo .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, China

    Wei Li

  3. South China Agricultural University, Guangzhou, China

    Hui Wang

  4. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Joseph, I.A., Khan, M.A., Luo, H. (2020). Orange Leaf Diseases Identification Using Digital Image Processing. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_27

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  • DOI: https://doi.org/10.1007/978-981-15-5577-0_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5576-3

  • Online ISBN: 978-981-15-5577-0

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