ArticleTropical animal health and production2025
Real-time ultrasound and RGB imaging integration for non-invasive prediction of ribeye area in Tropical Crossbred Brahman Cattle.
Article in Tropical animal health and production, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study integrates real-time ultrasound (RTU) imaging for live animal assessment with RGB (Red, Green, and Blue) imaging for post-slaughter ribeye analysis, aiming to bridge the gap between pre-slaughter prediction and carcass evaluation in high-lean beef production. The primary objective was to develop a predictive model for ribeye traits using RTU-derived measurements from crossbred Brahman cattle. A total of 74 cattle were studied, of which 15 ribeye steaks were available for RGB imaging and model development. Independent variables including backfat thicknesses (BF), ribeye area (REA), ribeye perimeter (REP), and ribeye depth (RED) were extracted from RTU images (denoted as u) using a portable ultrasound device (3.5-5.0 MHz). Post-slaughter RGB images of ribeye steaks (s) were captured using an iPhone 13 Pro, and analysed via Image-J software. Regression models were developed using SPSS 25.0 and validated using XLStat. Significant differences (p < 0.05) were observed between REA_u and REA_s yet a strong positive correlation (r = 0.960) confirmed agreement between two imaging technique. The resulting predictive equation for RGB-derived ribeye area was: REA_s = 38.815 + (0.932 × REA_u) with R2 = 0.921, adjusted R2 = 0.915 and the lowest standard error (SE = 1.350) among all the regression model used in this study. Model performance metrics further supported its reliability (RMSE = 1.350, MAPE = 1.049, MSE = 1.822). These results confirmed that REA_u is a reliable non-destructive predictor of ribeye trait, a valuable proof-of-concept tool in high-lean beef production systems. However, the model may be prone to overfitting, and further validation using a larger independent dataset is recommended in future studies.
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