Equivalence Between Optical Flow, the Unrest Index, and Walking Distance to Estimate the Welfare of Broiler Chickens DOI Creative Commons
Danilo Florentino Pereira, Irenilza de Alencar Nääs, Saman Abdanan Mehdizadeh

et al.

Animals, Journal Year: 2025, Volume and Issue: 15(9), P. 1311 - 1311

Published: May 1, 2025

Modern poultry production demands scalable and non-invasive methods to monitor animal welfare, particularly as broiler strains are increasingly bred for rapid growth, often at the expense of mobility health. This study evaluates two advanced computer vision techniques—Optical Flow Unrest Index—to assess movement patterns in chickens. Three commercial (Hybro®, Cobb®, Ross®) were housed controlled environments continuously monitored using ceiling-mounted video systems. Chicken movements detected tracked a YOLO model, with centroid data informing both Index distance walked metrics. Optical velocity metrics (mean, variance, skewness, kurtosis) extracted Farnebäck algorithm. Pearson correlation analyses revealed strong associations between variables traditional indicators, average showing strongest Index. Among evaluated strains, Cobb® demonstrated variance Index, indicating distinct profile. The equipment’s camera’s slight instability had minimal effect on measurement. Still, its walking accredits it an effective method high-resolution behavioral monitoring. supports integration technologies into precision livestock systems, offering foundation predictive welfare management scale.

Language: Английский

ACMSPT: Automated Counting and Monitoring System for Poultry Tracking DOI Creative Commons
Edmanuel Cruz, Miguel Hidalgo-Rodriguez, Adiz Mariel Acosta-Reyes

et al.

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(3), P. 86 - 86

Published: March 19, 2025

The poultry industry faces significant challenges in efficiently monitoring large populations, especially under resource constraints and limited connectivity. This paper introduces the Automated Counting Monitoring System for Poultry Tracking (ACMSPT), an innovative solution that integrates edge computing, Artificial Intelligence (AI), Internet of Things (IoT). study begins by collecting a custom dataset 1300 high-resolution images from real broiler farm environments, encompassing diverse lighting conditions, occlusions, growth stages. Each image was manually annotated used to train YOLOv10 object detection model with carefully selected hyperparameters. trained then deployed on Orange Pi 5B single-board computer equipped Neural Processing Unit (NPU), enabling on-site inference real-time tracking. performance evaluated both small- commercial-scale sheds, achieving precision 93.1% recall 93.0%, average time 200 milliseconds. results demonstrate ACMSPT can autonomously detect anomalies movement, facilitating timely interventions while reducing manual labor. Moreover, its cost-effective, low-connectivity design supports broader adoption remote or resource-limited environments. Future work will focus improving adaptability extreme conditions extending this approach other livestock management contexts.

Language: Английский

Citations

0
Equivalence Between Optical Flow, the Unrest Index, and Walking Distance to Estimate the Welfare of Broiler Chickens DOI Creative Commons
Danilo Florentino Pereira, Irenilza de Alencar Nääs, Saman Abdanan Mehdizadeh

et al.

Animals, Journal Year: 2025, Volume and Issue: 15(9), P. 1311 - 1311

Published: May 1, 2025

Modern poultry production demands scalable and non-invasive methods to monitor animal welfare, particularly as broiler strains are increasingly bred for rapid growth, often at the expense of mobility health. This study evaluates two advanced computer vision techniques—Optical Flow Unrest Index—to assess movement patterns in chickens. Three commercial (Hybro®, Cobb®, Ross®) were housed controlled environments continuously monitored using ceiling-mounted video systems. Chicken movements detected tracked a YOLO model, with centroid data informing both Index distance walked metrics. Optical velocity metrics (mean, variance, skewness, kurtosis) extracted Farnebäck algorithm. Pearson correlation analyses revealed strong associations between variables traditional indicators, average showing strongest Index. Among evaluated strains, Cobb® demonstrated variance Index, indicating distinct profile. The equipment’s camera’s slight instability had minimal effect on measurement. Still, its walking accredits it an effective method high-resolution behavioral monitoring. supports integration technologies into precision livestock systems, offering foundation predictive welfare management scale.

Language: Английский

Citations

0