Earticle

초록

영어

Computer vision applications in the field of agriculture science are gaining importance. The paper presents a method for recognition of paddy varieties from bulk paddy grain image samples based on color texture features extracted from color co-occurrence matrices. The color texture features are obtained from H, S and I color planes and their combinations. The feature set is reduced based on contribution of features to the recognition accuracy. The reduced feature set of the HS plane includes Energy, Entropy and Correlation features from Hue plane and Energy, Entropy, Contrast, and Correlation features from Saturation plane. The paddy grain images are recognized using a multilayer feed-forward artificial neural network. The considered fifteen paddy varieties have given the recognition accuracy of 92.33%. The work is useful in developing a machine vision system for agriculture produce market and developing multimedia applications in agriculture sciences.

목차

Abstract
 1. Introduction
 2 Proposed Method
  2.1 Image Acquisition
  2.2 Feature Extraction
  2.3 Feature Selection
  2.4 Recognition of Paddy Varieties
 3 Results and Discussion
  3.1 Recognition Using Color Texture Features
  3.2 Recognition Using Reduced Color Texture Features
 4. Conclusion
 References

키워드

저자

• Basavaraj S. Anami [ Department of Computer Science and Engineering, K L E Institute of Technology, Hubli, 580030, India ]
• Naveen N. M. [ Department of Information Science and Engineering, K L E Institute of Technology, Hubli, 580030, India ]
• N. G. Hanamaratti [ Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwar, 580002, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605