Research Article | OPEN ACCESS
Image Segmentation and Maturity Recognition Algorithm based on Color Features of Lingwu Long Jujube
1Yutan Wang, 1Jiangming Kan, 1Wenbin Li and 1Chuandong Zhan
1School of Technology, Beijing Forestry University, Beijing 100083, P.R. China
2School of Mechanical Engineering, Ningxia University, Yinchuan 750021, P.R. China
Advance Journal of Food Science and Technology 2013 12:1625-1631
Received: August 19, 2013 | Accepted: August 27, 2013 | Published: December 05, 2013
Abstract
Fruits’ recognition under natural scenes is a key technology to intelligent automatic picking. In this study, an image segmentation method based on color difference fusion in RGB color space was proposed in order to implement image segmentation and recognition maturity intelligently according to Lingwu long jujubes’ color features under the complex environment. Firstly, the three-dimensional histograms of each color component which is widely used in color space currently are compared; and then the jujubes’ red area and non-red area was extracted respectively, thus, the whole target area is obtained by sum of those areas; then, watershed algorithm combined with mathematical morphology distance and gradient was utilized to overcome adhesion and occlusion phenomena; finally, the maturity level was recognized by the established recognition model of Lingwu long jujubes. The segmentation was tested through 100 sample set and 93.27% of precision rate was attained, so was correct estimating rate of maturity level recognition above 90%. The results indicate that a smaller average segmentation error probability is in this method, which is more efficient in the extraction and recognition of jujubes with red and green and the problem of segmentation and maturity level judgment of adhesive fruits is solved by the method as well.
Keywords:
Color difference, image segmentation, lingwu long jujubes, mature level, watershed transform,
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.
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ISSN (Online): 2042-4876
ISSN (Print): 2042-4868 |
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