Development and Effect Verification of Beef Cattle Carcass Grading Camera Equipment System
DOI:
https://doi.org/10.54097/fbem.v12i1.13758Keywords:
Beef, Snowflake beef grading, Snowflake beef grading camera equipment.Abstract
This paper first discusses the development status of beef cattle carcass grading and photographic equipment at home and abroad, then carries out the equipment design and model establishment, and finally conducts sensory carcass assessment and grading and environmental simulation detection experiments through experiments, and uses the colorimeter and beef carcass intelligent grading system to detect the color difference L*, a*, b* values of meat samples in different environments, color space RGB and HSV color parameters, compare the influence of different environments on meat color parameters and grading results, and verify the feasibility of beef carcass grading photography equipment. The results show that the overall values of the equipment group are at a stable level and in line with the sensory evaluation, so the beef carcass grading camera equipment can effectively avoid the influence of external objective factors, such as: light and dark, poor light source, limiter and framing distance. At the same time, it reduces the interference of operators in the classification process and other problems such as the subjective negligence of the operator on the classification results. The project design equipment structure is simple and reliable, automation equipment is easy to operate and low cost, the new beef cattle carcass grading photographic equipment, suitable for promotion and mass production into the market, can improve labor production efficiency, promote the development of local enterprises’ meat industry, increase enterprise income, reduce the production cost of enterprises, to promote the standardization and unification of beef cattle industry grading system.
Downloads
References
Hao Chen, Chunjie Wang, Smujid, et al. Research Progress of Beef Quality and Its Influencing Factors. Chinese Journal Of Animal Nutrition[J], 2021,(2): 669-678.
Jeyamkondan S, Kranzler G A,Lakshmikanth A. Predicting Beef Tenderness with Computer Vision. Agricultural Engineering[J], 2001.
Pannier L, Van d W T M, van der Steen F.T.H.J., et al. Prediction of chemical intramuscular fat and visual marbling scores with a conveyor vision scanner system on beef portion steaks. Meat Science[J], 2023.
Shimabukuro M, Kano A, Komine H, et al. Verification of image analysis accuracy of beef carcass cross section with new beef carcass camera. Nihon Chikusan Gakkaiho[J], 2022,(2): 125-132.
Huisi Li, Yingyao Li, Yuemin Yao, et al. Application of a new type of rock oil content proportion calculation based on HSV color model. Digital Technologies and Applications[J], 2022,(12): 53-55.
Guangyan Tang. The implementation of the conversion of RGB color model to HSV color model in VB. Scientific and technical information[J], 2009,(02).
Ge Yang, Jialong Zheng,Ying Wang. Human detection and tracking algorithm based on HSV and RGB color space. Automation Technology and Application[J], 2022,(009): 041.
Lan Du, Binhua Chen, Da Wang, et al. A measurement and calibration method of colorimeter in CIELAB color space. Shanghai Metrology Testing[J], 2022,(4): 4.
Lei Niu, Zhisheng Zhang, Haipeng Li, et al. An overview of the research on the value-added segmentation and quality evaluation of beef carcass. Meat Research[J], 2010,(4): 3.
Pin Ma, Qi Liang, Pengcheng Wen, et al. Effect of oxygen-containing packaging on the color stability of yak meat. Food and Fermentation Industry[J], 2016,(9): 7.
Meng Wei, Songshan Zhang, Peng Xie, et al. Sensory evaluation and quality prediction model construction of nine local snowflake yellow beef consumers. Food Industry and Technology[J], 2022,(6): 9.
Gui Zhu, Yongsheng Han, Zhiqiong Zhu, et al. Analysis of the current situation and prospect of domestic Wagyu snowflake beef industry. Modern Animal Husbandry Science and Technology[J], 2022,(5): 5.
Weimin Ma, He Zhu,Yanxia Xing. Research on Beef Marbling Grading Design Technology Based on Computer Vision. Journal of Shandong Agricultural Management Cadre College[J], 2022,(004): 039.








