Subscribe Now Subscribe Today
Science Alert
 
FOLLOW US:     Facebook     Twitter
Blue
   
Curve Top
Information Technology Journal
  Year: 2011 | Volume: 10 | Issue: 2 | Page No.: 267-275
DOI: 10.3923/itj.2011.267.275
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

Abstract:
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.
 [Fulltext PDF]   [Fulltext HTML]   [XML: Abstract + References]   [References]   [View Citation]  [Report Citation]
 RELATED ARTICLES:
  •    The RFM-based Institutional Customers Clustering: Case Study of a Digital Content Provider
  •    Static Hand Gesture Recognition for Human Computer Interaction
  •    A Multi Layer Perceptron Neural Network Trained by Invasive Weed Optimization for Potato Color Image Segmentation
  •    A Novel Pattern Recognition Approach Based on Immunology
  •    Identification of Sugarcane Nodes Using Image Processing and Machine Vision Technology
  •    K-Means Clustering to Improve the Accuracy of Decision Tree Response Classification
  •    Feature Extraction and Classification of Objects in the Rosette Pattern Using Component Analysis and Neural Network
  •    Image Thresholding Using Weighted Parzen-Window Estimation
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

COMMENTS
02 February, 2012
karthick:
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.....
12 April, 2016
Seth lfc:
Hello can i get this project code on matlab fully and its pdf. Thank you
15 January, 2018
RANGASWAMY H:
I need code on leaf disease identification.
 
COMMENT ON THIS PAPER
 
 
 

 

 
 
 
 
 
 
 
 
 

 
 
 
 

Curve Bottom