ArticleSensors (Basel, Switzerland)2019
Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Smart Animal Welfare: A Review of Sensing Technologies, Deployment Challenges, and AI-Driven Insights.Sensors (Basel, Switzerland) · 2026Review
- SowPostureDS: A Multi-Class Image Dataset for YOLO-Based Detection of Sow Postures in diverse Farrowing Systems.Scientific data · 2026Article
- Sensor-Based Precision Feeding Systems in Animal Production: Technologies and Applications.Animals : an open access journal from MDPI · 2026Review
- Cross-Breed Few-Shot Learning for Pig Detection via Improved YOLOv7 and CycleGAN-Based Sample Generation.Biology · 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
- StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information.Sensors (Basel, Switzerland) · 2026Article
- ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.Journal of animal science · 2026Review
- Sustainability and future outlook of the Philippine pig industry.Journal of advanced veterinary and animal research · 2025Article
- PigStressNet: A Real-Time Lightweight Vision System for On-Farm Heat Stress Monitoring via Attention-Guided Feature Refinement.Sensors (Basel, Switzerland) · 2025Article
- Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments.Animals : an open access journal from MDPI · 2025Article
- Instance segmentation and automated pig posture recognition for smart health management.Journal of animal science and technology · 2025Article
- A Sheep Behavior Recognition Approach Based on Improved FESS-YOLOv8n Neural Network.Animals : an open access journal from MDPI · 2025Article
- Primiparous sow behaviour on the day of farrowing as one of the primary contributors to the growth of piglets in early lactation.Scientific reports · 2024Article
- Detecting tail biters by monitoring pig screams in weaning pigs.Scientific reports · 2024Article
- Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets.PloS one · 2024Article
- Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL.Nature communications · 2023Article
- Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows.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
- Integrated Damage Location Diagnosis of Frame Structure Based on Convolutional Neural Network with Inception Module.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93.
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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.