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

## TL;DR

This paper presents a deep learning method to automatically measure beef carcass weight using images, improving efficiency in meat processing.

## Contribution

A novel pipeline combining deep learning segmentation and LASSO regression for accurate hot carcass weight prediction in tropical beef cattle.

## Key findings

- The YOLOv11 model achieved high segmentation accuracy (IoU = 0.92, Precision = 0.98).
- The HCW prediction model reached R² = 0.84 and MAPE = 5.77%.
- The pipeline is practical and scalable for real-time carcass evaluation.

## Abstract

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.