Earticle

초록

영어

In this paper, lesion areas affected by anthracnose are segmented using segmentation techniques, graded based on percentage of affected area and neural network classifier is used to classify normal and anthracnose affected on fruits. We have considered three types of fruit namely mango, grape and pomegranate for our work. The developed processing scheme consists of two phases. In the first phase, segmentation techniques namely thresholding, region growing, K-means clustering and watershed are employed for separating anthracnose affected lesion areas from normal area. Then these affected areas are graded by calculating the percentage of affected area. In the second phase texture features are extracted using Runlength Matrix. These features are then used for classification purpose using ANN classifier. We have conducted experimentation on a dataset of 600 fruits’ image samples. The classification accuracies for normal and affected anthracnose fruit types are 84.65% and 76.6% respectively. The work finds application in developing a machine vision system in horticulture field.

목차

Abstract
 1. Introduction
 2. Proposed Methodology
  2.1. Image Acquisition
  2.2. Segmentation Techniques
  2.3. Feature Extraction
  2.4. Classifier
 3. Results and Discussions
  3.1 Grading of Image Samples
  3.2. Identification Efficiency based on Reduced RM Texture Features
  3.3. Average Identification Efficiency based on Reduced RM Texture Features
 4. Conclusions
 References

키워드

저자

• Jagadeesh Devdas Pujari [ S.D.M.College of Engg. &Tech ]
• Rajesh Yakkundimath [ K.L.E.Institute of Technology ]
• Abdulmunaf Syedhusain Byadgi [ University of Agricultural Sciences ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605