Plants Identification by Leaf Shape using GLCM, Gabor Wavelets and PCA
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2016 by IJETT Journal|
|Year of Publication : 2016|
|Authors : Akshay A Patil, K. S.Bhagat
|DOI : 10.14445/22315381/IJETT-V37P222|
Akshay A Patil, K. S.Bhagat"Plants Identification by Leaf Shape using GLCM, Gabor Wavelets and PCA", International Journal of Engineering Trends and Technology (IJETT), V37(3),140-143 July 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Plants are essentials for life on Earth. Different species of plants can be distinguished with the help of leaf shapes, petals barks, and fruits. A digital recognition of plant species is a now a days in demand for various purposes. A new method for recognizing and identifying plants has been devised. Leaf images are pre-processed to remove noise using median filtering on different image planes separately. Gabor wavelet and Gray-Level Co-Occurrence Matrix (GLCM) texture features are used to recognize/identify leaf shape, for classification Decision Tree Classifier is used. We found improvement in results by utilizing combination of GLCM and Gabor features. Effective discriminative power is increased by dimensionality reduction with the help of Principal component analysis (PCA) and Decision tree as a classifier. This intelligent system provides accurate results within in less time by utilizing photographs of plants leaves, to identify tree species. The real time database is prepared for the experimental use. The database contains various leaves with various shapes, colours and size. Experiment is carried out with the different leaves of different classes and tested results are quite good except real time database.
Akshay A Patil, K. S.Bhagat,”Plants Identification by Leaf Shape using GLCM ,Gabor Wavelets and PCA.,” (communicated and accepted) International Journal of Engineering Trends and Technology (IJETT)
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Plants, leaf shape, digital image processing, GLCM, Gabor wavelets, Principal component analysis, decision tree.