Online evaluation of a commercial video image analysis system (Computer Vision System) to predict beef carcass red meat yield and for augmenting the assignment of USDA yield grades. United States Department of Agriculture

Abstract

Objective quantification of differences in wholesale cut yields of beef carcasses at plant chain speeds is important for the application of value-based marketing. This study was conducted to evaluate the ability of a commercial video image analysis system, the Computer Vision System (CVS) to 1) predict commercially fabricated beef subprimal yield and 2) augment USDA yield grading, in order to improve accuracy of grade assessment. The CVS was evaluated as a fully installed production system, operating on a full-time basis at chain speeds. Steer and heifer carcasses (n = 296) were evaluated using CVS, as well as by USDA expert and online graders, before the fabrication of carcasses into industry-standard subprimal cuts. Expert yield grade (YG), online YG, CVS estimated carcass yield, and CVS measured ribeye area in conjunction with expert grader estimates of the remaining YG factors (adjusted fat thickness, percentage of kidney-pelvic-heart fat, hot carcass weight) accounted for 67, 39, 64, and 65% of the observed variation in fabricated yields of closely trimmed subprimals. The dual component CVS predicted wholesale cut yields more accurately than current online yield grading, and, in an augmentation system, CVS ribeye measurement replaced estimated ribeye area in determination of USDA yield grade, and the accuracy of cutability prediction was improved, under packing plant conditions and speeds, to a level close to that of expert graders applying grades at a comfortable rate of speed offline.

The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle

“…LI et al, (1997) showed that brittleness of meat could be determined by vision texture analysis. Similarly, investigations have shown this method can be used to analyze meat, determine marbling score and evaluate MLD area (NEWMAN, 1984;KUCHIDA et al, 1991;SHACKELFORD et al, 1998;SHIRANITA et al, 2000;KARNUAH et al, 2001;CANNELL et al, 2002;TEIRA et al, 2003). BOZKURT et al, (2006) used digital image analysis technology regarding prediction of body weight from body measurements in beef cattle.…”

Use of X-Ray Computed Tomography (CT) in UK Sheep Production and Breeding

“…Different VIA systems have been studied outside the UK, mostly in beef cattle, to predict on-line carcass value and by the use of additional systems also aspects of meat eating quality (e.g. Cannell et al, 2002;Shackelford et al, 2003;Steiner et al, 2003;Vote et al, 2003, Allen & Finnerty, 2001Allen, 2005). While there are also a few studies on instrumental grading of lamb carcasses (Hopkins, 1996, Stanford et al, 1998, Brady et al, 2003Hopkins et al, 2004, Cunha et al, 2004, none of them had been applied to UK conditions for lamb carcasses until very recently.…”

Research on Beef Skeletal Maturity Determination Based on Shape Description and Neural Network

“…For detection of quality degree of beef carcass, researches in this area began in the early 80s. Many people have studied on the detection of beef marbling, muscle color and fat color by computer vision [6]- [8]. Some previous research show that only few people had carried out the research on image automated segmentation algorithm for cartilage and bone areas and the automatic identification of skeletal maturity grade.…”