Abstract

Simple SummaryWith the advent of artificial intelligence, the poultry sector is gearing up to adopt and embrace sensor technologies to enhance the production and the welfare of birds. Automated tracking and tracing of poultry birds has several advantages in poultry farms: overcoming the subjectivity of human measurements, enhancing the ability to provide quality care for the birds during their life on the farm, providing the ability to predict events and thereby enabling timely interventions, and many more. However, the technologies behind automated tracking systems are not ripe due to the lags in algorithms and practical implementation issues. This mini review provides a brief critical assessment of the current and recent advancements of automated tracking systems in the poultry industry and offers an outlook on future directions.The world’s growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animals on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of artificial intelligence (AI)-assisted technology individualised, and per-herd assessments of livestock became possible and accurate. Various studies have shown that cameras linked with specialised systems of AI can properly analyse flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on recent advancements, this review explores the aspects of AI in the detection, counting, and tracking of poultry in commercial and research-based applications.

Highlights

  • Today’s demands for increased livestock production result in various challenges for the animals they pertain to

  • artificial intelligence (AI) technologies enable the identification of individual broilers [15], or laying hens, among hundreds of birds via videos irrespective of similar sizes, shapes, and colours of the feathers

  • When turkeys are raised in artificially confined environments, they become more prone to cannibalistic behaviours that can be distinguished through pecking and movement patterns

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Summary

Introduction

Today’s demands for increased livestock production result in various challenges for the animals they pertain to. Societal pressures towards sustainability influence minimal inputs for poultry farming aspects such as land, labour, and natural resource usage These efforts may lead to increased poultry production with decreased production time and resource usage, but they have unintentionally led to the proliferation of harmful genetic alterations and the increase in associated diseases. Considering the difference between food accessibility and hunger for some people, and for farmers, rejection of chickens at slaughterhouses can be a great source of profit loss This statistic makes a huge difference for the animals, as it suggests that millions of chickens bred for meat suffer from unmanaged, painful, and possibly deadly medical (pathological) conditions each year.

Need for Automated Poultry Surveillance
Artificial Intelligence in Poultry Monitoring
Computer Vision Technology
Components of Machine Vision for Poultry Tracking
Detection of Broiler Movements through Optical Flow Patterns
Increasing Poultry Productivity through Time-Series Data Mining
Image Analysis of Broiler Chicken Behaviour at Different Feeders
Detection of Poultry Diseases Using Deep Learning Systems and Image Analysis
Infrared Receiver Assessments of Keel Bone Fractures in Laying Hens
Evaluation of Laying Hens’ Light Preferences
Deep Learning System Detection of Pecking Activity in Grouped-Housed Turkeys
Tracking and Stocking Density Estimation
Challenges and Future Research Directions
Conclusions
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