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October 13, 2025Tropical Animal Science JournalOpen Access

Digital Innovation in Predicting Live Body Weight of Female Ongole-Grade Cattle Using Pixel Area and Morphometric Analysis

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Authors

MAMd. Ibrahim AliBABALUH MEDYABRATA ATMAJADHDoni Herviyanto

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Overview

This research demonstrates regression models predicting body weight in Ongole-Grade cattle, highlighting the accuracy of digital methods over conventional measurements.

Key Points

  • Digital morphometric analysis provides a novel, cost-effective way to monitor body weight in Ongole-Grade cattle.
  • The study validated regression models with a mean absolute percentage error (MAPE) ranging from 1.76% to 4.89% for body weight prediction.
  • Strong correlations (r=0.80–0.91) were established between body weight and morphometric traits, particularly chest girth.
  • The quadratic regression model outperformed other approaches, achieving an R² of 0.93, while conventional methods were comparable.

Cite This Study

Ali et al. (2025) studied this question.

synapsesocial.com/papers/68ed4e04d3b1bfa344c60179https://doi.org/10.5398/tasj.2025.48.6.500
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The development of a body weight prediction method for Ongole Crossbred cattle using a meta-analysis and field experiment approach2024
  2. 2Statistical tool for the estimation of body weight of lactating cows from 2D photographs2024
  3. 3Optimizing Ongole Grade Cattle weight prediction through Principal Component Analysis2024 · 3 citations
  4. 4Measurement of body size parameters and body weight prediction in beef cattle based on image analysis2024 · 1 citations
  5. 5Optimizing Body Weight Prediction in Bali Cattle Using Morphometric Traits and Principal Component Regression2026