Surface Defects Detection of Red Jujube Based on Near-Infrared Vision System

Article Preview

Abstract:

Quality evaluation of agricultural and food products is important for processing, inventory control, and marketing. Fruit surface defects are important quality factors for the jujube industry, especially for high quality jujubes such as Xinjiang red jujube. This paper presents the development and test results of a machine vision system for automatic jujube surface defects detection. Unlike other near-infrared spectrometric approaches, the developed machine vision system uses reflective near-infrared image to evaluate jujube quality by analyzing two-dimensional images. Near-infrared image, vision algorithms and a variety of operational details of the system, including cameras, optics, illumination, and fruit carrier are presented. The complete machine vision system has been built, and the experimental results show that the designed machine vision system is feasible to detect the defects of jujubes.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

573-577

Citation:

Online since:

February 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] S.P. Kang, A.R. East, F.J. Trujillo, Colour vision system evaluation of bicolour fruit: A case study with 'B74' mango, Postharvest Biology and Technology, Vol. 49, pp.77-85, (2008).

DOI: 10.1016/j.postharvbio.2007.12.011

Google Scholar

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

DOI: 10.1016/j.jfoodeng.2006.12.007

Google Scholar

[3] M.Z. Abdullah, J. Mohamad-Saleh, A.S. Fathinul-Syahir, Discrimination and classification of fresh-cut starfruits using automated machine vision system, Journal of Food Engineering, Vol. 76 pp.506-523, (2006).

DOI: 10.1016/j.jfoodeng.2005.05.053

Google Scholar

[4] H.K. Mebatsion, J. Paliwal, D.S. Jayas, Evaluation of variations in the shape of grain types using principal components analysis of the elliptic Fourier descriptors, Computers and Electronics in Agriculture, Vol. 80, pp.63-70, (2012).

DOI: 10.1016/j.compag.2011.10.016

Google Scholar

[5] A. Herrero-Langreo, E. Fernández-Ahumada, J.M. Roger, Combination of optical and non-destructive mechanical techniques for the measurement of maturity in peach, Journal of Food Engineering, Vol. 108, pp.150-157, (2012).

DOI: 10.1016/j.jfoodeng.2011.07.004

Google Scholar

[6] Kavdir I., Guyer D. E, Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features, Biosystem Engineering , Vol. 89, No. 3, pp.331-344, (2004).

DOI: 10.1016/j.biosystemseng.2004.08.008

Google Scholar

[7] Mehl, P.M., Chen, Y.R., Kim, M.S., Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations, Journal Food Engineering, Vol. 61, pp.67-81, (2004).

DOI: 10.1016/s0260-8774(03)00188-2

Google Scholar

[8] Zhan Hui, Li Xiaoyu, Zhou Zhu, Detection of chestnut defect based on data fusion of near-infrared spectroscopy and machine vision, Transactions of the CSAE, Vol. 27, No. 2, p.345 – 349, 2011(in Chinese).

Google Scholar

[9] Dah-Jye Lee, Robert Schoenberger, James Archibald, Development of a machine vision system for automatic date grading using digital reflective near-infrared imaging, Journal of Food Engineering, Vol. 86 , pp.388-398, (2008).

DOI: 10.1016/j.jfoodeng.2007.10.021

Google Scholar

[10] Yousef Al Ohali, Computer vision based date fruit grading system: Design and implementation, Journal of King Saud University-Computer and Information Sciences, Vol. 23, pp.29-36, (2011).

DOI: 10.1016/j.jksuci.2010.03.003

Google Scholar

[11] Li Jiangbo, Rao Xiuqin, Ying Yibin, Detection of navel surface defects based on illumination-reflectance model, Transactions of the CSAE, Vol. 27, No. 7, pp.338-342, 2011(in Chinese).

Google Scholar