A Review about Behavioural Indicators of Stress in Broilers: Insights from Digital Monitoring Technologies
Keywords:
broiler welfare, detection, pattern recognition, physiology, video recordingsAbstract
Early detection and management of stress are very important to ensure welfare in broiler production systems and significantly influences poultry’s health, behaviour, and productivity. Main stress-related behaviours, such as social interaction, excessive pecking, increased aggression, and reduced locomotion, are best analysed using smart video surveillance systems and machine learning algorithms capable of real-time data processing and pattern recognition. Also, the integration of wearable sensors (e.g., temperature and heart rate monitors) represents a complementary tool to enhance the precision and reliability of behavioural data. This review offers a comprehensive study of current digital solutions aimed to enhance animal welfare through automated, continuous, and non-invasive stress monitoring in broiler flocks. It discusses the use of video cameras equipped with computer vision and behavioural pattern recognition algorithms, presenting real-time applications for identifying signs of social isolation, aggression, and abnormal movement patterns. Additionally, the review emphasizes the role of machine learning algorithms in training neural networks to analyse large datasets generated from video recordings and behavioural reports. Finally, the review examines the wearable sensors tools like temperature and heart rate monitors, emphasizing how they enhance visual observations by providing valuable information about internal physiological states. Each of these technologies is evaluated in terms of accuracy, feasibility, and implementation challenges in commercial poultry systems. The integration of such tools can significantly enhance our ability to monitor broiler welfare dynamically, paving the way for predictive management strategies and improved animal care.
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