ArticleScientific reports2020
Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 94 citations in OpenAlex.
- Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Video-Based Feeding Demand Sensing and Offline Feeding Strategy Evaluation in Group-Housed Pigs: A Single-Pen Proof-of-Concept Study Using Oriented Object Detection.Animals : an open access journal from MDPI · 2026Article
- Computer Vision Applied to the Analysis of Pig Behavior Patterns in an Air-Conditioned Environment.Animals : an open access journal from MDPI · 2026Article
- Accelerating sow nursing behavior monitoring with modified YOLO11n architecture and TensorRT integration.Porcine health management · 2026Article
- Characterising responses in group-housed pigs to Salmonella typhimurium infection through integrated computer vision-based behavioural monitoring and statistical analyses.Porcine health management · 2026Article
- Modeling and predicting water consumption in fattening pigs using autoregressive moving average with external parameters.Scientific reports · 2026Article
- StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information.Sensors (Basel, Switzerland) · 2026Article
- Developing and Assessing the Performance of a Machine Learning Model for Analyzing Drinking Behaviors in Minipigs for Experimental Research.Sensors (Basel, Switzerland) · 2026Article
- Instance segmentation and automated pig posture recognition for smart health management.Journal of animal science and technology · 2025Article
- Article
- RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review.Journal of animal science and technology · 2025Review
- Based on improved joint detection and tracking of UAV for multi-target detection of livestock.Heliyon · 2024Article
- Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach.Animals : an open access journal from MDPI · 2024Article
- An efficient visual servo tracker for herd monitoring by UAV.Scientific reports · 2024Article
- An Integrated Gather-and-Distribute Mechanism and Attention-Enhanced Deformable Convolution Model for Pig Behavior Recognition.Animals : an open access journal from MDPI · 2024Article
- Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets.PloS one · 2024Article
- Determining the posture and location of pigs using an object detection model under different lighting conditions.Translational animal science · 2024Article
- Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing.Sensors (Basel, Switzerland) · 2023Article
- Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors.Animals : an open access journal from MDPI · 2023Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Changes in pig behaviours are a useful aid in detecting early signs of compromised health and welfare. In commercial settings, automatic detection of pig behaviours through visual imaging remains a challenge due to farm demanding conditions, e.g., occlusion of one pig from another. Here, two deep learning-based detector methods were developed to identify pig postures and drinking behaviours of group-housed pigs. We first tested the system ability to detect changes in these measures at group-level during routine management. We then demonstrated the ability of our automated methods to identify behaviours of individual animals with a mean average precision of [Formula: see text], under a variety of settings. When the pig feeding regime was disrupted, we automatically detected the expected deviations from the daily feeding routine in standing, lateral lying and drinking behaviours. These experiments demonstrate that the method is capable of robustly and accurately monitoring individual pig behaviours under commercial conditions, without the need for additional sensors or individual pig identification, hence providing a scalable technology to improve the health and well-being of farm animals. The method has the potential to transform how livestock are monitored and address issues in livestock farming, such as targeted treatment of individuals with medication.
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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.