INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.7, No.9, Aug. 2015

Automatic Detection of Surface Defects on Citrus Fruit based on Computer Vision Techniques

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Author(s)

Mohana S.H., Prabhakar C.J.

Index Terms

Citrus stem-end detection;Circle fitting;Mean shift segmentation

Abstract

In this paper, we present computer vision based technique to detect surface defects of citrus fruits. The method begins with background removal using k-means clustering technique. Mean shift segmentation is used for fruit region segmentation. The candidate defects are detected using threshold based segmentation. In this stage, it is very difficult to differentiate stem-end from actual defects due to similarity in appearance. Therefore, we proposed a novel technique to differentiate stem-end from actual defects based on the shape features. We conducted experiments on our citrus data set captured in controlled environment. The experiment results demonstrate that our technique outperforms the existing techniques.

Cite This Paper

Mohana S.H., Prabhakar C.J.,"Automatic Detection of Surface Defects on Citrus Fruit based on Computer Vision Techniques", IJIGSP, vol.7, no.9, pp.11-19, 2015.DOI: 10.5815/ijigsp.2015.09.02

Reference

[1]Jiangbo Li, Xiuqin Rao, Fujie Wang, Wei Wu and Yibin Ying. Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, Vol. 82, pages 59-69, 2013.

[2]Dae Gwan Kim, Thomas F. Burks, Jianwei Qin and Duke M. Bulanon. Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural and Biological Engineering. Vol. 2, No. 3, pages 41-50, 2009.

[3]Fernando Lopez-Garcia, Gabriela Andreu-Garcia, Jose Blasco, Nuria Aleixos and Jose-Miguel Valiente. Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, Vol. 71, pages 189-197, 2010.

[4]Jiangbo Li, Xiuqin Rao and Yibin Ying. Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, Vol. 78, pages 38-48, 2011.

[5]Jianwei Qin, Thomas F. Burks, Mark A. Ritenour and W. Gordon Bonn. Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering. Vol. 93, pages 183-191, 2009.

[6]Jose J. Lopez, Maximo Cobos and Emanuel Aguilera. Computer based detection and classification of flaws in citrus fruits. Neural Computing and Applications, Vol. 20, No. 7, pages 975-981, 2011.

[7]Blasco J., Aleixos N. and Molto E. Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering, Vol. 81, pages 535-543, 2007.

[8]Jianwei Qin, Thomas F. Burks, Xuhui Zhao, Nikhil Niphadkar and Mark A. Ritenour. Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, Vol. 108, pages 87-93, 2012.

[9]Gomez-Sanchis J., Gomez-Chova L., Aleixos N., Camps-Calls G., Montesinos-Herrero C., Molto E. and Blasco J. Hyperscpectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, Vol. 89, pages 80-86, 2008.

[10]Dorin Comaniciu and Peter Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pages 603-619, 2002.

[11]Fukunaga K. and L.D Hostetler. The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Trans. Information Theory, Vol. 21, pages. 32-40, 1975.

[12]Baohua Zhang, Wenqian Huang, Liang gong, Jiangbo Li, Chunjiang Zhao, Chengliang Liu and Danfeng Huang. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. Journal of Food Engineering, Vol. 146, pages 143-151, 2015.

[13]Wenqian Huang, Jiangbo Li, Qingyan Wang and Liping Chen. Development of a multispectral imaging system for online detection of bruises on apples. Journal of Food Engineering, Vol. 146, pages 62-71, 2015.

[14]Shimrat, M., "Algorithm 112: Position of point relative to polygon" 1962, Communications of the ACM Volume 5 Issue 8, Aug. 1962.