Revolutionize Hen Care With Our AI-Powered Health Monitoring System
Experience the pinnacle of innovation as UTS researchers unveil their world-leading AI-based system, revolutionizing the monitoring of cage-free hens to enhance their health and overall welfare.
Many Australians have developed a fondness for free-range eggs, seeking options where hens have the liberty to wander freely across verdant fields or expansive barns instead of being confined within cages. Nonetheless, this enhanced freedom can also pose greater challenges to the health and welfare of the hens.
The University of Technology Sydney (UTS) researchers have made groundbreaking strides in artificial intelligence by creating a cutting-edge system that monitors and analyzes the movements and behaviors of cage-free hens. This state-of-the-art technology aims to enhance the health and welfare of the hens while effectively mitigating potential risks.
Dr. Jian Zhang and his research group at the Multimedia Data Analytics Lab within the Global Big Data Technologies Centre, housed at UTS Tech Lab, are actively engaged in creating an advanced video surveillance solution. The team’s groundbreaking work was recently tested at an egg farm in Windsor, NSW, where they successfully conducted a trial run of their real-time monitoring system.
The project receives funding from Australian Eggs, an esteemed non-profit organization its members own. Australian Eggs is dedicated to utilizing farmer levies and public funds to support research and development initiatives for the egg industry.
“The farm involved in this project, which is experimenting with the technology, maintains an average of 8,000 hens distributed across multiple sheds. The constant monitoring of hen behavior poses a challenge for the farm staff,” stated Dr. Zhang. While cage-free hens enjoy more freedom to roam and forage, there is a potential risk of flocking together and forming a ‘pile-up’ situation, which can lead to hens being smothered. Dr. Zhang further mentioned, “Although infrequent, if a pile-up does occur, there are currently no warning systems in place to notify farmers about such incidents.”
Given the lack of knowledge regarding adequate controls and predictors of risk, the egg industry aims to develop strategies that effectively mitigate these concerns. Alongside the real-time monitoring system being developed, Australian Eggs has allocated funds for an ongoing research project to identify these risk factors.
The newly implemented system promptly notifies farm personnel of any crowding behavior while monitoring the hens’ access to food and water, allowing for observations of changes in behavior and detecting any signs of immobility in individual hens, which could indicate potential injuries or illnesses.
Expressing his enthusiasm, Rowan McMonnies, the Managing Director of Australian Eggs, remarked on the promising nature of this solution, as it can reduce labor requirements, minimize human intervention within the sheds, and enhance animal welfare.
“This significant investment in AI technology not only holds the promise of happier, healthier, and more productive hens but also facilitates improved farm management and cost reduction,” stated Mr. McMonnies.
The current trial version of the system is equipped with four cameras: two positioned inside the sheds and two placed outside. The system’s scalability has been considered during its design and can be expanded per the area’s requirements. The technology uses pattern recognition algorithms to count and monitor flock density and behavior accurately.
The UTS team comprises experts in poultry and animal behavior who assist in analyzing and interpreting hen behavior. The subsequent stage of the project will involve a more in-depth examination of activities such as drinking, feeding, foraging, and standing.
Due to their sociable and gregarious nature, poultry necessitates the development of an ethogram, a comprehensive inventory of behaviors that encompasses both individual and interactive actions. This ethogram will enable better monitoring of growth and welfare, explained Dr. Zhang.
Additionally, there are intentions to create a mobile application capable of alerting supervisors to potential issues. The app aims to enhance the system by incorporating machine learning technology and user feedback.
The UTS team will conduct a more extensive trial encompassing multiple farms in the forthcoming months. Once the trials conclude, they aim to commercialize the technology, allowing for broader implementation. Furthermore, there exists the possibility of expanding this technology to other agricultural sectors.
To make this system even better, several suggestions can be considered:
Continuous Improvement: The researchers should continue refining the AI-based system by collecting feedback from farmers, industry experts, and animal welfare organizations. This feedback can help identify areas for improvement and ensure that the system meets the evolving needs of the industry.
Expansion of Monitoring Parameters: While the current system focuses on monitoring crowding behavior, access to food and water, and signs of immobility, additional parameters related to hen health and welfare can be included. This could involve monitoring factors such as dust levels, temperature, and air quality within the sheds to ensure optimal conditions for the hens.
Collaboration and Knowledge Sharing: Encouraging collaboration between researchers, industry stakeholders, and other academic institutions can foster knowledge sharing and accelerate advancements in this field. Sharing data, best practices, and research findings can help refine the system and promote widespread adoption across the egg industry.
Integration with Existing Farm Management Systems: Aim To enhance farm management practices, the AI-based system can be integrated with existing farm management software. This integration would enable seamless data sharing, analysis, and decision-making, empowering farmers to make informed choices about their flock’s welfare and productivity.
Education and Training: Providing training programs and resources to farmers and farm staff on the effective use of the AI-based system can maximize its benefits. This can include workshops, tutorials, and user manuals that guide users in understanding and interpreting the data generated by the system.
By incorporating these suggestions, the AI-based system can be further improved, leading to enhanced monitoring capabilities, increased efficiency, and improved welfare outcomes for cage-free hens.
