What question did this study set out to answer?

The aim is to develop a more accurate and non-invasive method for predicting sheep body weight using depth images.

A computer vision end-to-end method for predicting sheep body weight from depth images

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Randomized trial evaluates a computer vision method for predicting sheep weight, suggesting improvements in livestock management.

The aim is to develop a more accurate and non-invasive method for predicting sheep body weight using depth images.
Evaluated ConvNet method based on EfficientNet-B3 for end-to-end body weight prediction.
Performance assessed using Coefficient of Determination (R²), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).
Investigated performance influences using a Generalized Linear Mixed Model (GLMM).
Achieved R² of 0.97, MAE of 1.68 kg, and MAPE of 6.74%, showing competitive results.
Model demonstrated effective prediction across various coat colors, farms, and weight ranges.
Saliency maps indicated important features for model interpretability.

Sousa et al. (2026) studied this question.

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