ArticleJournal of animal science2019
A novel automated system to acquire biometric and morphological measurements and predict body weight of pigs via 3D computer vision.
Article in Journal of animal science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare.Frontiers in veterinary science · 2021Pooled it
- A Novel Approach of Pig Weight Estimation Using High-Precision Segmentation and 2D Image Feature Extraction.Animals : an open access journal from MDPI · 2025Article
- A Review of Visual Estimation Research on Live Pig Weight.Sensors (Basel, Switzerland) · 2024Review
- Improving Efficiency: Automatic Intelligent Weighing System as a Replacement for Manual Pig Weighing.Animals : an open access journal from MDPI · 2024Article
- Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets.PloS one · 2024Article
- Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review.Animals : an open access journal from MDPI · 2023Review
- Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Automatic Detection of Group Recumbency in Pigs via AI-Supported Camera Systems.Animals : an open access journal from MDPI · 2023Article
- A farmer-friendly tool for estimation of weights of pigs kept by smallholder farmers in Uganda.Tropical animal health and production · 2023Article
- Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle.Animals : an open access journal from MDPI · 2023Article
- Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera.Translational animal science · 2022Article
- Predicting ribeye area and circularity in live calves through 3D image analyses of body surface.Journal of animal science · 2022Article
- Towards the Estimation of Body Weight in Sheep Using Metaheuristic Algorithms from Biometric Parameters in Microsystems.Micromachines · 2022Article
- Impact of housing environment and management on pre-/post-weaning piglet productivity.Journal of animal science · 2022Review
- ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.Journal of animal science · 2022Review
- ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.Journal of animal science · 2022Review
- Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume.Animals : an open access journal from MDPI · 2021Article
- Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method.Sensors (Basel, Switzerland) · 2021Article
- ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images.Journal of animal science · 2021Review
- Forecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor camera.Translational animal science · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer vision applications in livestock are appealing since they enable measurement of traits of interest without the need to directly interact with the animals. This allows the possibility of multiple measurements of traits of interest with minimal animal stress. In the current study, an automated computer vision system was devised and evaluated for extraction of features of interest, as body measurements and shape descriptors, and prediction of body weight in pigs. From the 655 pigs that had data collected 580 had more than 5 frames recorded and were used for development of the predictive models. The cross-validation for the models developed with data from nursery and finishing pigs presented an R2 ranging from 0.86 (random selected image) to 0.94 (median of images truncated on the third quartile), whereas with the dataset without nursery pigs, the R2 estimates ranged from 0.70 (random selected image) to 0.84 (median of images truncated on the third quartile). However, overall the mean absolute error was lower for the models fitted without data on nursery animals. From the body measures extracted from the image, body volume, area, and length were the most informative for prediction of body weight. The inclusion of the remaining body measurements (width and heights) or shape descriptors to the model promoted significant improvement of the predictions, whereas the further inclusion of sex and line effects were not significant.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.