ArticleAnimals : an open access journal from MDPI2024
Pig Weight Estimation Method Based on a Framework Combining Mask R-CNN and Ensemble Regression Model.
Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evaluation of hybrid models based on image segmentation and inference for pig weight estimation.Frontiers in artificial intelligence · 2026Article
- A Novel Approach of Pig Weight Estimation Using High-Precision Segmentation and 2D Image Feature Extraction.Animals : an open access journal from MDPI · 2025Article
- Real-Time Pig Weight Assessment and Carbon Footprint Monitoring Based on Computer Vision.Animals : an open access journal from MDPI · 2025Article
- Body Temperature Detection of Group-Housed Pigs Based on the Pairing of Left and Right Ear Roots in Thermal Images.Animals : an open access journal from MDPI · 2025Article
- Predicting Body Weight from Birth to Old Age in Giant Pandas Using Machine Learning.Animals : an open access journal from MDPI · 2024Article
- A Review of Visual Estimation Research on Live Pig Weight.Sensors (Basel, Switzerland) · 2024Review
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Authors and funding
6 authors.
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Abstract
Using computer vision technology to estimate pig live weight is an important method to realize pig welfare. But there are two key issues that affect pigs' weight estimation: one is the uneven illumination, which leads to unclear contour extraction of pigs, and the other is the bending of the pig body, which leads to incorrect pig body information. For the first one, Mask R-CNN was used to extract the contour of the pig, and the obtained mask image was converted into a binary image from which we were able to obtain a more accurate contour image. For the second one, the body length, hip width and the distance from the camera to the pig back were corrected by XGBoost and actual measured information. Then we analyzed the rationality of the extracted features. Three feature combination strategies were used to predict pig weight. In total, 1505 back images of 39 pigs obtained using Azure kinect DK were used in the numerical experiments. The highest prediction accuracy is XGBoost, with an MAE of 0.389, RMSE of 0.576, MAPE of 0.318% and R2 of 0.995. We also recommend using the Mask R-CNN + RFR method because it has fairly high precision in each strategy. The experimental results show that our proposed method has excellent performance in live weight estimation of pigs.
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