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     Advance Journal of Food Science and Technology


Enhanced Machine Vision System for Ripe Fruit Detection Based on Robotic Harvesting

R. Thendral and A. Suhasini
Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu 608002, India
Advance Journal of Food Science and Technology  2015  11:841-849
http://dx.doi.org/10.19026/ajfst.7.2520  |  © The Author(s) 2015
Received: October 15, ‎2014  |  Accepted: November ‎3, ‎2014  |  Published: April 10, 2015

Abstract

The proposed study intends to provide an efficient algorithm for the instruction of an automatic robot arm to choose the ripe fruits on the tree. Steps involved in this study are recognizing and locating the ripe fruits from the leaf and branch portions by using an efficient machine vision algorithm. Initially, discrete wavelet transform is used for better preserving of edges and fine details in the given input image. Then RGB, HSV, L*a*b* and YIQ color spaces were studied to segment the ripe fruits from the surrounding objects. Finally, the results showed that ‘I’ component of the YIQ color space has the best criterion for recognizing the fruit from the foliage. The fruit segmentation based on machine vision has an occlusion problem. In this proposed method these problems are also examined.

Keywords:

Color based segmentation, color spaces, image processing, machine vision, ripe fruits, robotic harvesting,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
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