Digital Innovation in Predicting Live Body Weight of Female Ongole-Grade Cattle Using Pixel Area and Morphometric Analysis
Abstract
This study is the first to validate digital morphometric analysis combined with linear, quadratic, and allometric regression models for predicting body weight (BW) in Ongole-Grade cattle under smallholder field conditions, focusing on productive-age females as breeding stock. The objective was to develop and validate regression-based predictive models using digital image-derived traits and to compare their accuracy with conventional measurements and existing formulas. A total of 204 female Ongole-Grade cattle were measured manually and with ImageJ-based morphometrics. All measurements were standardized to a reference age of 12 months using an allometric adjustment. Traits assessed included BW, body length (BL), withers height (WH), chest girth (CG), chest depth (CD), rump height (RH), and rump width (RW). BW showed strong positive correlations with morphometric traits (r=0.80–0.91), with CG as the strongest predictor. Conventional and image-derived measurements were comparable for WH, BL, CG, CD, and RH (p>0.05), while RW differed significantly (p=0.01). Mean differences were small (≤0.8 cm), and the mean absolute percentage error (MAPE) ranged from 1.76% to 4.89%, confirming the reliability of digital imaging. The quadratic regression model (CG² + BL²), which outperformed the linear, allometric, and pixel-area–based approaches (MAPE=4.68%; R²=0.93). In contrast, the Schoorl formula substantially overestimated BW (MAPE=37.76%), while the pixel-area model showed only moderate accuracy (R²=0.63). Overall, digital morphometric analysis provides a novel, non-invasive, and cost-effective tool for cattle monitoring, with refinement of pixel area-based features recommended.
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References
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