Automatic Dead Zone Detection in 2-D Leaf Image Using Clustering and Segmentation Technique

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Rajat Kumar Sahoo 1,* Ritu Panda 1 Ram Chandra Barik 2 Samrendra Nath Panda 3

1. Department of Botany, Vikash Degree College

2. Department of Computer Science & Engineering, Vikash Institute of Technology

3. Department of Chemistry, Vikash Group of Institution

* Corresponding author.


Received: 23 May 2018 / Revised: 11 Jun. 2018 / Accepted: 26 Jun. 2018 / Published: 8 Oct. 2018

Index Terms

Hue Saturation Value (HSV), k-means, Saliency feature, Red Green Blue (RGB), Segmentation, L*a*b* color model, Grey threshold


Plant is a gift of almighty to the living being in the earth. Leaf is an essential component for any types of plant including crops, fruit and vegetables. Before the scheduled decay of the leaf due to deficiency there are patches of dead zone spot or sections generally visible. This paper introduces a novel image based analysis to identify patches of dead zone spot or sections generally visible due to deficiency. Clustering, colour object based segmentation and colour transformation techniques using significant salient features identification are applied over 12 plant leaves collected naturally from gardens and crop fields. Hue, saturation and Value based and L*a*b* colour model based object analysis is being applied over diseased leaf and portion of leaf to identify the dead zone automatically. Derivative based edge analysis is being applied to identify the outline edge and dead zone segmentation in leaf image. K-means clustering has played an important role to cluster dead zone using colour based object area segmentation.

Cite This Paper

Rajat Kumar Sahoo, Ritu Panda, Ram Chandra Barik, Samrendra Nath Panda, " Automatic Dead Zone Detection in 2-D Leaf Image Using Clustering and Segmentation Technique", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.10, pp. 11-30, 2018. DOI: 10.5815/ijigsp.2018.10.02


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