Earticle

초록

영어

In order to obtain more value added products, a product quality control is essentially required Many studies show that quality of agriculture products may be reduced from many sources. In this paper I express Technological Strategies uses Sony cyber-shot DSC W350 camera to captured symptoms of Brassica sp. diseased leaf images and sort out the diseases using support vector machine. There is a need to classify and regrow them for the use of future generations. So it is very indispensable to develop a spontaneous system to identify species correctly. This paper proposes an algorithm for recognition of diseased leaf and show the name of disease through image processing and classification technique using Support Vector Machine (SVM) classifier. The K-Mean Clustering method is used for Segmentation and Gray-level co-occurrence matrix (GLCM) is used for the feature extraction in leaves. The work is carried out using 2 species of leaves in MATLAB environment.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1 Plant Materials and Disease Identification
  2.2 Image Acquisition of Diseased Plants
  2.3 Image Pre-Processing and Segmentation
  2.4 Feature Extraction
  2.5 Shape Feature Extraction
  2.6 Color Feature Extraction
  2.7 Statistical Analysis
  2.8 Classification
 3. Results
 4. Discussion
 5. Future Implications
 References

키워드

저자

• Palak Sharma [ Research Scholar CSE Department DAV University Jalandhar, Punjab, India ]
• Nishi [ Assistant Professor CSE Department DAV University Jalandhar, India ]
• Ashutosh Sharma [ Assistant Professor Agriculture Department DAV University, Jalandhar, Punjab, India ]
• Puja Rattan [ Assistant Professor Agriculture Department DAV University, Jalandhar, Punjab, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605