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Information Technology Journal

Year: 2011 | Volume: 10 | Issue: 2 | Page No.: 267-275
DOI: 10.3923/itj.2011.267.275

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Authors


Dheeb Al Bashish

Country: Jordan

Malik Braik

Country: Jordan

Sulieman Bani-Ahmad

Country: Jordan

Keywords


  • hue saturation intensity
  • K-means
  • neural networks
  • image segmentation
  • Leaf diseases
  • texture features
Research Article

Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural-networks-based Classification

Dheeb Al Bashish, Malik Braik and Sulieman Bani-Ahmad
The aim of this study is to design, implement and evaluate an image-processing-based software solution for automatic detection and classification of plant leaf diseases. Studies show that relying on pure naked-eye observation of experts to detect and classify such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The methodology of the proposed solution is image-processing-based and is composed of four main phases; in the first phase we create a color transformation structure for the RGB leaf image and then, we apply device-independent color space transformation for the color transformation structure. Next, in the second phase, the images at hand are segmented using the K-means clustering technique. In the third phase, we calculate the texture features for the segmented infected objects. Finally, in the fourth phase the extracted features are passed through a pre-trained neural network. As a testing step we use a set of leaf images taken from Al-Ghor area in Jordan. Present experimental results indicate that the proposed approach can significantly support an accurate and automatic detection and recognition of leaf diseases. The developed Neural Network classifier that is based on statistical classification perform well in all sampled types of leaf diseases and can successfully detect and classify the examined diseases with a precision of around 93%. In conclusion, the proposed detection models based neural networks are very effective in recognizing leaf diseases, whilst K-means clustering technique provides efficient results in segmentation RGB images.
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How to cite this article

Dheeb Al Bashish, Malik Braik and Sulieman Bani-Ahmad, 2011. Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural-networks-based Classification. Information Technology Journal, 10: 267-275.

DOI: 10.3923/itj.2011.267.275

URL: https://scialert.net/abstract/?doi=itj.2011.267.275

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Comments


karthick Reply
02 February, 2012

sir me doing M.E then i am doing project for detection for disease in one leaf .what and all algorithm me used pso ,genetic, abc algorithm and which one is best ,,,,r any other algorithm used ..please send have any coding for this project help me.....

Seth lfc Reply
12 April, 2016

Hello can i get this project code on matlab fully and its pdf. Thank you

RANGASWAMY H Reply
15 January, 2018

I need code on leaf disease identification.

chirunjeevi Reply
06 December, 2019

i want the project code

Floris Olivier Reply
18 March, 2021

Github Link?

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