Detection of egg stains based on local texture feature clustering
Keywords:
eggs, eggshell dirt stains, computer vision, local texture feature, FCM, egg classifyingAbstract
The quality of egg is mainly influenced by the dirt adhering to its shell. Even with good farm-management practices and careful handling, a small percentage of dirty eggs will be produced. The purpose of this research was to detect the egg stains by using image processing technique. Compared to the color values, the local texture was found to be much more adept at accurately segmenting of the complex and miscellaneous dirt stains on the egg shell. Firstly, the global threshold of the image was obtained by two-peak method. The irrelevant background was removed by using the global threshold and the interested region was acquired. The local texture information extracted from the interested region was taken as the input of fuzzy C-means clustering for segmentation of the dirt stains. According to the principle of projection, the area of dirt stains on the curved egg surface was accurately calculated. The validation experimental results showed that the proposed method for classifying eggs in terms of stain has the specificity of 91.4% for white eggs and 89.5% for brown eggs. Keywords: eggs, eggshell dirt stains, computer vision, local texture feature, FCM, egg classifying DOI: 10.25165/j.ijabe.20181101.2592 Citation: Yang Q H, Jia M M, Xun Y, Bao G J. Detection of egg stains based on local texture feature clustering. Int J Agric & Biol Eng, 2018; 11(1): 199–205.References
[1] Soltani M, Omid M, Alimardani R. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network. Journal of Food Science and Technology, 2015; 52(5): 3065–3071.
[2] Zhang W, Wu X, Qiu Z, He Y. A novel method for measuring the volume and surface area of egg. Journal of Food Engineering, 2015; 170: 160–169.
[3] Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69–76.
[4] Ketelaere B D, Coucke P, Baerdemaeker J D. Eggshell crack detection based on acoustic resonance frequency analysis. Journal of Agricultural Engineering Research, 2000; 76(2): 157–163.
[5] Cheng J, Xie L, Ying Y. Eggshell crack detection based on the time-domain acoustic signal of rolling eggs on a step-plate. Journal of Food Engineering, 2015; 153(1): 53–62.
[6] Chen M, Zhang L, Xu H. On-line detection of blood spot introduced into brown-shell eggs using visible absorbance spectroscopy. Biosystems Engineering, 2015; 131: 95–101.
[7] Detector: Abnormal Egg Detector. Available: https://www.nabel.co.jp/ english/product/abd.html. Accessed on [2016/5/10]. (in Chinese)
[8] Dehrouyeh M H, Omid M, Ahmadi H, Mohtasebi S S, Jamzad M. Grading and quality inspection of defected eggs using machine vision. International Journal of Advanced Science & Technology, 2010; 16: 23–30.
[9] Mor-Mur M, Yuste J. Emerging bacterial pathogens in meat and poultry: An overview. Food and Bioprocess Technology, 2010; 3(1): 24.
[10] Wesley I V, Muraoka W T. Time of entry of salmonella, and campylobacter, into the Turkey Brooder House. Food and Bioprocess Technology, 2011; 4(4): 616–623.
[11] Patel VC, McClendon R W, Goodrum J W. Detection of blood spots and dirt stains in eggs using computer vision and neural networks. Applied Engineering in agriculture, 1996; 12(2): 253–258.
[12] Patel V C, Mcclendon R W, Goodrum J W. Color computer vision and
artificial neural networks for the detection of defects in poultry eggs. Artificial Intelligence Review, 1998; 12(1): 163–176.
[13] Garcia-Alegre M C, Ribeiro A, Guinea D, Cristobal G. Eggshell defects detection based on color processing. Proceedings of SPIE - The International Society for Optical Engineering, 2007; 3966: 280–287.
[14] Ribeiro A, García-Alegre M C, Guinea D, Cristobal G. Automatic rules generation by GA for eggshell defect classification. Networks, 2000; 4: 5.
[15] Mertens K, Ketelaere B D, Kamers B, Bamelis F R, Kemps B J, Verhoelst E M, et al. Dirt detection on brown eggs by means of color computer vision. Poultry Science, 2005; 84(10): 1653–1659.
[16] Lunadei L, Ruiz-Garcia L, Bodria L, Guidetti R. Automatic identification of defects on eggshell through a multispectral vision system. Food and Bioprocess Technology, 2012; 5(8): 3042–3050.
[17] Arivazhagan S, Shebiah R N, Sudharsan H, Kannan R R, Ramesh R. External and internal defect detection of egg using machine vision. Journal of Emerging Trends in Computing and Information Sciences, 2013; 4(3): 257–262.
[18] Cen Y K. Research on quality inspection of eggs based on machine vision. Master dissertation. Hangzhou: Zhejiang University, 2006. (in Chinese)
[19] Ma L, Fan Y L. Texture image analysis. Beijing: Science Press, 2009. p231. (in Chinese)
[20] Wang W F, Ma L, Yang L. Liver contour extraction using modified snake with morphological multiscale gradients. 2008 International Conference on Computer Science and Software Engineering, 2008; 6: 117–120.
[21] Duan Q, Chen P C, Zou Q H. Method for egg surface area estimation based on computer vision. Journal of Anhui Agricultural University, 2013; 40(2): 342–344. (in Chinese)
[22] Tu K, Pan L Q, Yang J L, Su Z P, Yu X. Dirt detection on brown eggs based on computer vision. Journal of Jiangsu University (Natural Science Edition), 2007; 28(3): 189–192. (in Chinese)
[23] United States Standards, Grades, and Weight Classes for Shell Eggs. AMS 56. 2000. Available: https://www.ams.usda.gov/ grades-standards/eggs. Accessed on [2016-5-12].
[2] Zhang W, Wu X, Qiu Z, He Y. A novel method for measuring the volume and surface area of egg. Journal of Food Engineering, 2015; 170: 160–169.
[3] Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69–76.
[4] Ketelaere B D, Coucke P, Baerdemaeker J D. Eggshell crack detection based on acoustic resonance frequency analysis. Journal of Agricultural Engineering Research, 2000; 76(2): 157–163.
[5] Cheng J, Xie L, Ying Y. Eggshell crack detection based on the time-domain acoustic signal of rolling eggs on a step-plate. Journal of Food Engineering, 2015; 153(1): 53–62.
[6] Chen M, Zhang L, Xu H. On-line detection of blood spot introduced into brown-shell eggs using visible absorbance spectroscopy. Biosystems Engineering, 2015; 131: 95–101.
[7] Detector: Abnormal Egg Detector. Available: https://www.nabel.co.jp/ english/product/abd.html. Accessed on [2016/5/10]. (in Chinese)
[8] Dehrouyeh M H, Omid M, Ahmadi H, Mohtasebi S S, Jamzad M. Grading and quality inspection of defected eggs using machine vision. International Journal of Advanced Science & Technology, 2010; 16: 23–30.
[9] Mor-Mur M, Yuste J. Emerging bacterial pathogens in meat and poultry: An overview. Food and Bioprocess Technology, 2010; 3(1): 24.
[10] Wesley I V, Muraoka W T. Time of entry of salmonella, and campylobacter, into the Turkey Brooder House. Food and Bioprocess Technology, 2011; 4(4): 616–623.
[11] Patel VC, McClendon R W, Goodrum J W. Detection of blood spots and dirt stains in eggs using computer vision and neural networks. Applied Engineering in agriculture, 1996; 12(2): 253–258.
[12] Patel V C, Mcclendon R W, Goodrum J W. Color computer vision and
artificial neural networks for the detection of defects in poultry eggs. Artificial Intelligence Review, 1998; 12(1): 163–176.
[13] Garcia-Alegre M C, Ribeiro A, Guinea D, Cristobal G. Eggshell defects detection based on color processing. Proceedings of SPIE - The International Society for Optical Engineering, 2007; 3966: 280–287.
[14] Ribeiro A, García-Alegre M C, Guinea D, Cristobal G. Automatic rules generation by GA for eggshell defect classification. Networks, 2000; 4: 5.
[15] Mertens K, Ketelaere B D, Kamers B, Bamelis F R, Kemps B J, Verhoelst E M, et al. Dirt detection on brown eggs by means of color computer vision. Poultry Science, 2005; 84(10): 1653–1659.
[16] Lunadei L, Ruiz-Garcia L, Bodria L, Guidetti R. Automatic identification of defects on eggshell through a multispectral vision system. Food and Bioprocess Technology, 2012; 5(8): 3042–3050.
[17] Arivazhagan S, Shebiah R N, Sudharsan H, Kannan R R, Ramesh R. External and internal defect detection of egg using machine vision. Journal of Emerging Trends in Computing and Information Sciences, 2013; 4(3): 257–262.
[18] Cen Y K. Research on quality inspection of eggs based on machine vision. Master dissertation. Hangzhou: Zhejiang University, 2006. (in Chinese)
[19] Ma L, Fan Y L. Texture image analysis. Beijing: Science Press, 2009. p231. (in Chinese)
[20] Wang W F, Ma L, Yang L. Liver contour extraction using modified snake with morphological multiscale gradients. 2008 International Conference on Computer Science and Software Engineering, 2008; 6: 117–120.
[21] Duan Q, Chen P C, Zou Q H. Method for egg surface area estimation based on computer vision. Journal of Anhui Agricultural University, 2013; 40(2): 342–344. (in Chinese)
[22] Tu K, Pan L Q, Yang J L, Su Z P, Yu X. Dirt detection on brown eggs based on computer vision. Journal of Jiangsu University (Natural Science Edition), 2007; 28(3): 189–192. (in Chinese)
[23] United States Standards, Grades, and Weight Classes for Shell Eggs. AMS 56. 2000. Available: https://www.ams.usda.gov/ grades-standards/eggs. Accessed on [2016-5-12].
Downloads
Published
2018-01-31
How to Cite
Yang, Q., Jia, M., Xun, Y., & Bao, G. (2018). Detection of egg stains based on local texture feature clustering. International Journal of Agricultural and Biological Engineering, 11(1), 199–205. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2592
Issue
Section
Information Technology, Sensors and Control Systems
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
