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Detection of Disease Using Block-Based Unsupervised Natural Plant Leaf Color Image Segmentation

  • Shitala Prasad
  • Piyush Kumar
  • Anuj Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

Abstract

A novel unsupervised color image segmentation method is proposed in this paper. First, the image is converted to HSI color model and then it is divided into 5x5 grid matrix resulting into 25 blocks of original leaf image. Each block is then processed separately and passed under an unsupervised segmentation. This segmentation is based on minimizing the energy of each region in the image. This gives the better result in cased of diseased leaf image dataset. This automated system is very much applicable in research work by the botanists specially working with crop diseases and production.

Keywords

Computer Vision Unsupervised Natural Image Segmentation Gaussian Mixture Model Leaf Disease Detection Histogram-based Segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shitala Prasad
    • 1
  • Piyush Kumar
    • 2
  • Anuj Jain
    • 3
  1. 1.Department of Electronics and Computer EngineeringIndian Institute of Technology, RoorkeeRoorkeeIndia
  2. 2.Department of Information TechnologyIndian Institute of Information TechnologyAllahabadIndia
  3. 3.Department of Computer Science and EngineerningNRI Institute of Technology and ManagmentGwaliorIndia

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