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Color Image Segmentation of Disease Infected Plant Images Captured in an Uncontrolled Environment

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Smart and Innovative Trends in Next Generation Computing Technologies (NGCT 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 828))

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Abstract

In the field of an automated vision system for integrated disease control in plants, accurate identification of plant diseases is very important. The symptoms of the occurrences of diseases appear in different parts of the plant. The analysis of segmented images of infected parts in plant images helps to take curative and/or corrective measures for the diseases. In this paper, we present a threshold-based segmentation using RGB-L*a*b* hybrid color space features to segment the plant diseases from the digitally acquired images. In this proposed method, without interfering with spatial features of the color image, the quantization and binarization had been done in L*a*b* color space image and it applied to RGB color image for the segmentation of the image. Initially, the acquired RGB color images had been transformed into L*a*b* color images. Then multilevel threshold had been applied on the a* component of L*a*b* image. On the basis of threshold value a*, components of a* had been quantized, and equivalent binary matrices had been created. These binary matrices had been applied individually to the R, G, and B components of RGB images separately and concatenated to perform color image segmentation of infected part of the disease in the plant image. Our calculation gives the prominent result to segment diseased part of the plant images captured in an uncontrolled environment.

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Authors and Affiliations

  1. Rungta College of Engineering and Technology, CSVTU, Bhilai, 490024, CG, India

    Toran Verma & Sipi Dubey

  2. DAV University, Jalandhar, 144012, Punjab, India

    Hiteshwari Sabrol

Authors
  1. Toran Verma
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  2. Sipi Dubey
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  3. Hiteshwari Sabrol
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Corresponding author

Correspondence to Toran Verma .

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Editors and Affiliations

  1. Indian Institute of Technology Patna, Patna, Bihar, India

    Pushpak Bhattacharyya

  2. University of Petroleum and Energy Studies, Dehradun, India

    Hanumat G. Sastry

  3. University of Petroleum and Energy Studies, Dehradun, India

    Venkatadri Marriboyina

  4. University of Petroleum and Energy Studies, Dehradun, India

    Rashmi Sharma

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Verma, T., Dubey, S., Sabrol, H. (2018). Color Image Segmentation of Disease Infected Plant Images Captured in an Uncontrolled Environment. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-10-8660-1_59

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  • DOI: https://doi.org/10.1007/978-981-10-8660-1_59

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8659-5

  • Online ISBN: 978-981-10-8660-1

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