ArticleAnimals : an open access journal from MDPI2025
A Novel Approach of Pig Weight Estimation Using High-Precision Segmentation and 2D Image Feature Extraction.
Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Batch-Level Average Market Weight Estimation and Interpretability Analysis for Pigs with Regional K-Means Clustering and AMFormer.Animals : an open access journal from MDPI · 2026Article
- A Novel Method for Estimating the Body Weight and Size of Sows Using 3D Point Cloud.Animals : an open access journal from MDPI · 2025Article
- Design and Verification of a Non-Contact Body Dimension Measurement System for Jiangquan Black Pigs Based on Dual-View Depth Vision.Animals : an open access journal from MDPI · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern livestock production, obtaining accurate body weight measurements for pigs is essential for feeding management and economic assessment, yet conventional weighing is laborious and can stress animals. To address these limitations, we developed a contactless image-based pipeline that first uses BiRefNet for high-precision background removal and YOLOv11-seg to extract the pig dorsal mask from top-view RGB images; from these masks we designed and extracted 17 representative phenotypic features (for example, dorsal area, convex hull area, major/minor axes, curvature metrics and Hu moments) and included camera height as a calibration input. We then compared eight machine-learning and deep-learning regressors to map features to body weight. The segmentation pipeline achieved mAP
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Registered trials
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