A computer vision system can be used to help poultry producers manage welfare and production behaviors.

In modern poultry farming, ensuring the welfare of broiler chickens is a top priority, not only from an ethical standpoint but also to optimize production outcomes. One cutting-edge approach to monitor broiler welfare involves using computer vision systems, which harness cameras and deep learning algorithms to observe and evaluate flock behaviors. By monitoring a range of welfare and production behaviors, this technology can offer valuable insights for poultry producers, enabling them to address health concerns promptly and maintain optimal management practices.

How Imaging Technology Assesses Broiler Welfare

The core of this system is a network of cameras strategically positioned around the poultry house. This setup includes cameras on the walls and ceiling, capturing both horizontal and top-down views of the flock. Each camera serves a specific function, providing data that deep learning algorithms process to identify behaviors critical to broiler welfare and productivity. With the ability to autonomously detect behaviors such as lameness, preening, stretching, eating, and drinking, the system grants producers a comprehensive overview of bird health and behavior, making it easier to compare flocks and refine management practices.

According to Hao Gan, an Assistant Professor at the University of Tennessee, the research project that developed this system placed cameras along the walls to detect lameness, while ceiling-mounted cameras monitored other activities. This approach allows for a holistic view of the flock, capturing important behaviors that indicate both physical and mental wellbeing in broilers.

Detecting Lameness with Horizontal Cameras

One of the primary welfare indicators for broilers is lameness, which can affect a bird’s ability to access food, water, and shelter, ultimately impacting its quality of life and growth. For this reason, the research focused on using side-view cameras mounted along the walls of the poultry house to track gait patterns. These cameras capture detailed footage of individual broilers' movements, which is then analyzed using deep learning technology.

The deep learning algorithm identifies key points on each broiler, generating a skeleton model to autonomously detect signs of lameness. With this setup, gait scores are assigned to individual birds on a scale from 0 to 3, where a score of 0 indicates no lameness, and 3 suggests a moderate level. In a controlled research setting, this system achieved an impressive 97.5% accuracy rate, making it highly reliable for identifying and grading lameness.

The ability to detect lameness early allows producers to take preventative measures, reducing the spread of leg health issues within a flock. This automated detection not only improves welfare but also enhances productivity by ensuring healthier, more mobile birds are able to reach feed and water stations efficiently.

Evaluating Eating, Drinking, Preening, and Stretching with Overhead Cameras

While the wall-mounted cameras excel at assessing lameness, the ceiling-mounted cameras capture a different set of behaviors. Positioned to provide an overhead view, these cameras track essential production behaviors such as feeding, drinking, preening, and stretching. Gan explains that as birds grow larger, it becomes increasingly difficult to visually differentiate individual birds, especially during feeding and drinking times, making a top-down perspective crucial for accurate observation.

During the research, the top-view camera system measured feeding and drinking times with 97.9% and 85.4% accuracy, respectively. Such data allows producers to track whether the flock is feeding and hydrating adequately, which is vital for maintaining proper growth rates and production outcomes. The system’s monitoring of preening and stretching, activities that indicate comfort and self-maintenance, was achieved with an accuracy of 88.1%. However, Gan notes that further research is needed to improve the classification accuracy for preening behaviors.

Preening and stretching are important indicators of broiler welfare, as they suggest that the birds are comfortable and free from stress. Regular stretching is also essential to maintain good physical health, as it prevents stiffness and encourages muscle development. By tracking these behaviors, producers gain insight into the general wellbeing of the flock, ensuring that the environment is conducive to natural behaviors and comfort.

Future Development and Potential Applications

This project builds on prior research, which involved a different camera system designed to monitor commercial broilers at both the individual and flock levels. Recognized for its innovation, this research was named one of the Phase 1 SMART Broiler winners in 2020, highlighting its potential to revolutionize poultry farming.

While the current system already boasts high accuracy levels, further advancements could make the technology even more precise and accessible. For instance, refining the algorithms to better classify complex behaviors, like preening, could enhance the system's ability to provide real-time, actionable data. Additionally, increasing the system's efficiency in distinguishing individual birds as they grow will allow for longer monitoring periods without requiring adjustments in camera placement or configuration.

Beyond broiler welfare, the implications of this technology could extend to other areas of poultry and livestock farming. By continuously improving deep learning and imaging capabilities, similar systems could be adapted for monitoring other farm animals, leading to industry-wide advancements in animal welfare.

The integration of imaging technology in poultry farming marks a promising step forward in the quest for better broiler welfare. By leveraging computer vision and deep learning algorithms, this system offers poultry producers a powerful tool to monitor and manage welfare-related behaviors in real time. Not only does this technology facilitate the early detection of health issues like lameness, but it also provides insights into everyday behaviors that reflect the birds' comfort and wellbeing.

As research progresses and the technology becomes more refined, it could pave the way for widespread adoption, ultimately setting a new standard for animal welfare in the poultry industry. This innovation demonstrates that with the right tools, the industry can achieve a balanced approach to productivity and ethical responsibility, fostering healthier flocks and a more sustainable future for poultry farming.