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<p>The application of computer vision and deep learning in the meat processing industry enables automated carcass evaluation. This study aimed to develop and validate a deep learning-based pipeline for automatic carcass segmentation and prediction of hot carcass weight (HCW) in tropical beef cattle. A total of 598 RGB images of bovine half-carcasses were collected under commercial slaughterhouse conditions and manually annotated to delineate carcass boundaries. For segmentation, a YOLOv11 model was trained. From the segmented images, geometric and shape descriptors were extracted and subsequently used in a LASSO regression model to predict HCW. A strong segmentation performance was achieved, with an Intersection over Union (IoU) of 0.92 and a Precision of 0.98. For HCW prediction, the model achieved R² = 0.84 and MAPE = 5.77%. The integration of deep learning–based segmentation with regularized regression provides a practical and scalable approach for carcass evaluation. The combination of computer vision and statistical learning enables real-time, accurate prediction of beef carcass weight.</p>

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Deep learning-based image segmentation for predicting hot carcass weight in tropical beef cattle

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Gutierrez José de Freitas Assis,
  • Nathalia Farias de Souza,
  • Erica Beatriz Schultz,
  • Cris Luana de Castro Nunes,
  • André Henrique Franco Costa,
  • Antônio Almeida Santos Neto,
  • José Augusto Miranda Nacif,
  • Lucas Bragança da Silva,
  • Ricardo dos Santos Ferreira,
  • Mario Luiz Chizzotti

摘要

The application of computer vision and deep learning in the meat processing industry enables automated carcass evaluation. This study aimed to develop and validate a deep learning-based pipeline for automatic carcass segmentation and prediction of hot carcass weight (HCW) in tropical beef cattle. A total of 598 RGB images of bovine half-carcasses were collected under commercial slaughterhouse conditions and manually annotated to delineate carcass boundaries. For segmentation, a YOLOv11 model was trained. From the segmented images, geometric and shape descriptors were extracted and subsequently used in a LASSO regression model to predict HCW. A strong segmentation performance was achieved, with an Intersection over Union (IoU) of 0.92 and a Precision of 0.98. For HCW prediction, the model achieved R² = 0.84 and MAPE = 5.77%. The integration of deep learning–based segmentation with regularized regression provides a practical and scalable approach for carcass evaluation. The combination of computer vision and statistical learning enables real-time, accurate prediction of beef carcass weight.