Abstract
Aim at the agaricus bisporus postharvest automatic classification problem, this paper designed a kind of agaricus bisporus grading system based on machine vision, the system is mainly composed of machine vision system, mechanical system, automatic control system three parts, and analyzed the key technologies involved in every part. Extracted the feature parameters from the mushroom cap color, mushroom cap area and mushroom stem three aspects, combined with the classification standard, the final classification result is given by using edible fungus intelligent recognition platform, and then control the robot grabbing the agaricus bisporus into the corresponding classification box, the rate of accuracy reached over 88%. The results show that using machine vision based automatic grading system for the agaricus bisporus classification is feasible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aguirre, L., Frias, J.M., Barry-ryan, C., et al.: Modelling browning and brown spotting of mushrooms (agaricus Bisporus) stored in controlled environmental conditions using image analysis. J. Food Eng. 91(2), 280–286 (2009)
Huang, X., Jiang, S., Chen, Q., et al.: Identification of defect Pleurotus Geesteranus based on computer vision. Trans. CSAE 26(10), 350–354 (2010). (in Chinese with English abstract)
Zhou, Z., Huang, Y., Li, X., et al.: Automatic detecting and grading method of potatoes based on machine vision. Trans. CSAE 28(7), 178–183 (2012). (in Chinese with English abstract)
Hui, Z., Xiaoyu, L., Wei, W., et al.: Determination of chestnuts grading based on machine vision. Trans. CSAE 26(4), 327–331 (2010). (in Chinese with English abstract)
Zhang, Z., Niu, Z., Zhao, S., et al.: Weight grading of freshwater fish based on computer vision. Trans. CSAE 27(2), 350–354 (2011). (in Chinese with English abstract)
Hu, F., Liu, G., Hu, R., et al.: Quality grade detection in navel oranges based on machine vision and support vector machine. J. Beijing Univ. Technol. 40(11), 1615–1620 (2014)
Li, M., Fang, J., Qiao, Y., et al.: Automatic grading method of cucumber fruits based on machine vision. J. Agric. Mech. Res. 11, 229–233 (2016)
Wang, H., Xiong, J., Li, Z., Deng, J., Zou, X.: Potato grading method of weight and shape based on imaging characteristics parameters in machine vision system. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 32(8), 272–277 (2016). (in Chinese with English abstract)
Riquelme, M., Barreiro, P., Ruiz-altisent, M., et al.: Olive classification according to external damage using image analysis. J. Food Eng. 87(3), 371–379 (2008)
Gaston, E., FrÃas, J.M., Cullen, P.J., et al.: Visible-near infrared hyperspectral imaging for the identification and discrimination of brown blotch disease on mushroom (agaricus Bisporus) caps. J. Infrared Spectrosc. 33(8), 327–336 (2010)
Ge, L., Chen, H., Ren, J., et al.: The application of machine vision in the grading of mushrooms. Edible Fungi China 30(1), 8–9, 13 (2011)
Microvision. http://www.microvision.com.cn/
Otsu, N.A.: Threshold selection method from gray-scale histogram. IEEE Trans. Syst. Man Cybern. 62–66 (1978)
NY/T 1790—2009. Agaricus bisporus grades and specifications
Acknowledgment
Funds for this research was provided by Shandong Academy of Agricultural Sciences (SAAS) Youth Scientific Research Funds Project (2015YQN58), the Key Research and Development Plan of Shandong Province (2016CYJS03A01-1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 IFIP International Federation for Information Processing
About this paper
Cite this paper
Zheng, J., Feng, W., Liu, B., Wang, F. (2019). Design of Agaricus Bisporus Automatic Grading System Based on Machine Vision. In: Li, D. (eds) Computer and Computing Technologies in Agriculture X. CCTA 2016. IFIP Advances in Information and Communication Technology, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-030-06155-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-06155-5_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06154-8
Online ISBN: 978-3-030-06155-5
eBook Packages: Computer ScienceComputer Science (R0)
