Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry...

Area of Science:

• Animal Science
• Agricultural Engineering
• Food Science

Background:

• Accurate carcass composition prediction is crucial for the beef industry, impacting value-based marketing and processing efficiency.
• Traditional methods like the rib-eye camera (CCC) have limitations, especially in systems not routinely performing carcass dissection.
• Computer vision systems offer a non-invasive alternative for assessing carcass traits.

Purpose of the Study:

• To evaluate the potential of whole-side carcass cameras (HCC) compared to rib-eye cameras (CCC) and DXA for predicting primal and carcass composition in cull cows.
• To determine the accuracy of HCC for estimating total fat, lean, bone, subcutaneous fat (SQ), body cavity fat, and retail cut yield (RCY).
• To assess the combined performance of HCC and CCC, and the superiority of DXA.

Main Methods:

• Comparison of prediction accuracy (R²) between whole-side carcass cameras (HCC) and rib-eye cameras (CCC).
• Utilized dual energy X-ray absorptiometry (DXA) as a benchmark technology.
• Evaluated estimations for total fat, lean, bone, SQ, body cavity fat, RCY, and total lean meat yield (LMY).

Main Results:

• HCC showed similar total fat prediction to CCC but higher R² for lean (24.0%) and bone (61.6%).
• Combined HCC and CCC improved lean and bone predictions and LMY/RCY estimations by 25.3% compared to individual systems.
• DXA provided superior R² values (26.0% and 25.6%) compared to HCC alone or combined HCC/CCC.

Conclusions:

• Whole-side carcass cameras (HCC) are feasible for predicting primal and carcass composition in cull cows.
• HCC offers a valuable alternative for slaughter systems that do not routinely knife carcasses.
• Further research could explore optimizing HCC technology for broader industry application.