Development of ANN based Efficient Fruit Recognition Technique
Keywords:
fruit classification, gray level co-occurrence matrix, color, texture, artificial neural network
Abstract
Use of Image processing technique is increasing day by day in all fields and including the agriculture to classify fruits. Shape, color and texture are the image features which help in classification of fruits. This paper proposes an algorithm for fruits classification based on the shape, color and texture. For shape based classification of fruit area, perimeter, major axis length and minor axis length is calculated. Shape features are calculated by segmenting the object with the background using edge detection techniques. Mean and standard deviation is calculated for the color space like HSI, HSV which can be used for color base classification. Texture features is also calculated to enhance the classification process. Gray Level Co-occurrence Matrix (GLCM) is used to calculate texture features. Artificial neural network is used for classification of fruits. Artificial neural network classifies the fruits by comparing shape, color and texture feature provided at the time of training. MATLAB/ SIMULINK software is used to obtain result. Results obtained are better over the previous techniques and gives the accuracy upto 96%.
Downloads
- Article PDF
- TEI XML Kaleidoscope (download in zip)* (Beta by AI)
- Lens* NISO JATS XML (Beta by AI)
- HTML Kaleidoscope* (Beta by AI)
- DBK XML Kaleidoscope (download in zip)* (Beta by AI)
- LaTeX pdf Kaleidoscope* (Beta by AI)
- EPUB Kaleidoscope* (Beta by AI)
- MD Kaleidoscope* (Beta by AI)
- FO Kaleidoscope* (Beta by AI)
- BIB Kaleidoscope* (Beta by AI)
- LaTeX Kaleidoscope* (Beta by AI)
How to Cite
Published
2014-03-15
Issue
Section
License
Copyright (c) 2014 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.