Recent advances in the use of computer vision technology in the quality assessment of fresh meats

Abstract

Computer vision has emerged as a useful alternative to manual expert grading of meat in recent years. Manual grading by experts has a number of essential flaws that can be effectively mitigated with computer vision technology. Computer vision technology is a simple and affordable alternative that, while requiring some expertise, is not excessively technologically demanding. Computer vision technology has shown key attributes of flexibility and ease of compatibility allowing a wide range of meat quality assessment challenges to be successfully tackled. The simplest approaches involving visible light imaging and explicit statistical modelling have proven adequate on the vast majority of occasions. On the other hand, for difficult tasks, more expensive non-visible wavelength imaging and implicit statistical modelling are required. In addition, fully automatic image segmentation still remains a difficult problem in many instances, although image processing is becoming more powerful particularly as computationally demanding texture characterisation algorithms become more viable as computation speeds increase. Finally training and testing a computer vision system will require considerable groundwork as a substantial amount of image and independent meat quality data will be required for good model calibration and validation.