Real-time augmentation of USDA yield grade application to beef carcasses using video image analysis1,2

Abstract

In two phases, this study assessed the ability of two video image analysis (VIA) instruments, VIASCAN and Computer Vision System (CVS), to augment assignment of yield grades (YG) to beef carcasses to 0.1 of a YG at commercial packing plant speeds and to test cutout prediction accuracy of a YG augmentation system that used a prototype augmentation touchpanel grading display (designed to operate commercially in real-time). In Phase I, beef carcasses (n = 505) were circulated twice at commercial chain speeds (340 carcasses per hour) by 12 on-line USDA graders. During the first pass, on-line graders assigned a whole-number YG and a quality grade (QG) to carcasses as they would normally. During the second pass, on-line graders assigned only adjusted preliminary yield grades (APYG) and QG to carcasses, whereas the two VIA instruments measured the longissimus muscle area (LMA) of each carcass. Kidney, pelvic, and heart fat (KPH) was removed and weighed to allow computation of actual KPH percentage. Those traits were compared to the expert YG and expert YG factors. On-line USDA graders' APYG were closely related (r = 0.83) to expert APYG. Instrument-measured LMA were closely related (r = 0.88 and 0.94; mean absolute error = 0.3 and 0.2 YG units, for VIASCAN and CVS, respectively) to expert LMA. When YG were augmented using instrument-measured LMA and computed either including or neglecting actual KPH percentage, YG were closely related (r = 0.93 and 0.92, mean absolute error = 0.32 and 0.40 YG units, respectively, using VIASCAN-measured LMA; r = 0.95 and 0.94, mean absolute error = 0.24 and 0.34 YG units, respectively, using CVS-measured LMA) to expert YG. In Phase II, augmented YG were assigned (0.1 of a YG) to beef carcasses (n = 290) at commercial chain speeds using VIASCAN and CVS to determine LMA, whereas APYG and QG were determined by online graders via a touch-panel display. On-line grader YG (whole-number), expert grader YG (to the nearest 0.1 of a YG), and VIASCAN- and CVS-augmented YG (to the nearest 0.1 of a YG) accounted for 55, 71, 60, and 63% of the variation in fabricated yields of closely trimmed subprimals, respectively, suggesting that VIA systems can operate at current plant speeds and effectively augment official USDA application of YG to beef carcasses.

A review of the development and use of video image analysis (VIA) for beef carcass evaluation as an alternative to the current EUROP system and other subjective systems

“…A system developed on an animal population of a particular breed/breed type or produced under specific production conditions may not adequately explain variation observed in a different population. This is probably why the VIAscan CAS (Australia) and the CVS Beefcam® (USA) produced slightly different results on the same animals, in terms of subprimal cuts as a percentage of chilled side weight (R 2 = 60%, RSD = 1.3% and 63%, RSD = 1.2%, respectively) (Steiner et al, 2003). Differences in thresholds used for determining tissue boundaries could explain why the LMA measured by the CVS was~6% greater than that by the VIAscan.…”

“…Several studies have investigated how VIA can supplement the visual USDA carcass grading system for assignment of yield grade, (Belk, Scanga, Tatum, Wise, & Smith, 1998;Cannell et al, 1999Cannell et al, , 2002Steiner et al, 2003;Wulf & Page, 2000;Wyle et al, 2003) and quality grade (Wulf & Page, 2000;Wyle et al, 2003). Some of these integrated approaches have resulted in commercial developments as outlined in Section 3.5.…”

Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus

“…In meat industry the computer vision systems are used to determine the class of commercial slaughter half-carcasses of large animals according to EUROP grading system (Branschied, Hoereth, Baulain, Tholen, & Dobrowolski, 2004;Fortin et al, 2003;Pabiou et al, 2011;Steiner et al, 2003). Systems based on image analysis have been employed successfully in weight calculation, detection of fractures, contusions (bruising) and skin discolorations in chicken and turkey carcasses (Chao, Park, Chen, Hruschka, & Wheaton, 2000;Mollah, Hasan, Sala, & Ali, 2010).…”

Colour differences among carcasses graded with similar score for conformation and fatness

“…To avoid errors in carcass classification, there have been attempts to develop automatic classifiers of carcass grading (Steiner et al, 2003), but only video image analysis has been applied for the on-line classification of beef carcasses according to the EUROP scale for conformation and fat cover (Allen and Finnerty, 2001;Goyache et al, 2001).…”