Earticle

초록

영어

This paper describes Support Vector Machine (SVM) and Artificial Neural Network (ANN) based recognition and classification of visual symptoms affected by fungal disease. Color images of fungal disease symptoms affected on cereals like wheat, maize and jowar are used in this work. Different types of symptoms affected by fungal disease namely leaf blight, leaf spot, powdery mildew, leaf rust, smut are considered for the study. The developed algorithms are used to preprocess, segment, extract features from disease affected regions. The affected regions are segmented using k-means segmentation technique. Color texture features are extracted from affected regions and then used as inputs to SVM and ANN classifiers. The texture analysis is done using Color Co-occurrence Matrix. Tests are performed to classify image samples. Classification accuracies between 68.5% and 87% are obtained using ANN classifier. The average classification accuracies have increased to 77.5% and 91.16% using SVM classifier.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Image Set
  2.2. Image Preprocessing
  2.3. Segmentation
  2.4. Feature Extraction
  2.5. Classifiers
 4. Results and Discussion
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Jagadeesh D. Pujari [ S.D.M.College of Engineering & Technology Dharwar – 580 008, INDIA ]
• Rajesh Yakkundimath [ KLE.Institute of Technology Hubli – 580 030, INDIA ]
• Abdulmunaf S. Byadgi [ University of Agricultural Sciences, Dharwar – 580005, 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