As agriculture faces increasing pressure to meet sustainability goals while addressing labour shortages, the role of robotics and advanced technology has never been more crucial. The UK Agri-Tech Centre is supporting these innovations, collaborating with startups and researchers to develop practical solutions for the farming industry. The Cattle Hoof Monitor is a project aimed at improving cattle health and farm productivity through advanced monitoring systems. We talked to industry experts James Willcox and James Rogers about integrating automation and AI into farming, helping make farms more efficient and increasing sustainability.
Cattle Hoof Monitor Project spotlight
Strategic Theme: Intelligent Agriculture
Funding: £56,000, 12-Month Innovate UK Farming Innovation Research Starter
Consortium: Winson Agricultural, Rhyze Softworks Ltd. and the UK Agri-Tech Centre
Aims: The Cattle Hoof Monitor project uses a combination of thermal imagery and machine learning to detect early signs of lameness in cattle. Developed by a team of experts, including James Willcox and James Rogers, the system aims to automate the monitoring process, reducing the need for manual locomotion scoring and allowing farmers to intervene earlier, improving animal welfare and farm productivity.
James Willcox’s journey into agriculture started in an unexpected place: the military. After serving in defence, he transitioned into agri-tech, combining his knowledge of technology with his desire to work in an industry that contributes directly to feeding people and protecting the environment. His work on the Cattle Hoof Monitor project has been heavily influenced by his experience in developing robust systems for challenging conditions.
“I used to be in the army about 10 years ago, but by 2017, I decided to leave defence. I wanted to do something different, something that had more of an impact beyond military hardware. That’s when I went back to the Royal Agricultural University to study agri-tech, Willcox explained.
At university, I got interested in thermal imagery. It started as a small project, but it quickly grew into the idea of using thermal cameras to monitor lameness in dairy cattle.”
The motivation behind his switch to agriculture was rooted in a desire to work on something that had a more positive and wide-reaching effect:
“Part of what drew me to agriculture was the idea that it’s more than just a business—it’s about feeding people, protecting the environment and contributing to an important cause,” he added.
Speaking about the challenges encountered during the project, James noted the difficulties of working in a farm environment:
“Farm environments are incredibly tough on equipment. There’s a lot of muck at foot level and once it dries, it sets like concrete. We needed a system that could withstand these conditions, especially when monitoring cattle at ground level. With thermal imagery, we can detect inflammation before a cow even starts limping. That early intervention can make a huge difference in preventing further complications.”
James Rogers, a specialist in robotics and computer vision, is responsible for the technical development of the Cattle Hoof Monitor. With a background in embedded programming and machine learning models, James ensures that the system processes thermal imagery accurately in real time. His expertise in AI plays a crucial role in overcoming the unique challenges of applying technology to a farm setting.
“I’ve been working in robotics for years, mostly in computer vision, where we teach robots to understand the world around them. When I joined the Cattle Hoof Monitor project, it quickly became clear that farm environments are completely different from labs,” Rogers explained.
“Cows don’t behave predictably, and that’s a major challenge. The AI needs to handle animals moving unpredictably and still interpret the data correctly. It’s about teaching the system to make educated guesses when things don’t go perfectly,” he added.
This unpredictability was one of the toughest aspects:
“Farms are messy, dynamic places, and you can’t manually program for every situation. Machine learning is what makes it possible. The AI can learn from the environment and make generalisations, responding appropriately even if it’s never seen a particular scenario before.”
James also sees broader implications for AI in agriculture, though he notes that it won’t replace the need for people on farms:
“AI can take on some of the more repetitive tasks, but it’s not going to eliminate the need for human workers. For example, our system can save farmers hours by automating locomotion scoring, but at the end of the day, a farmer or a vet still needs to intervene to treat the cow. It’s about making things more efficient, not taking jobs away,” he explained.
The Cattle Hoof Monitor project is a promising example of how robotics and AI can address practical challenges in agriculture. By automating routine health checks, it has the potential to save farmers time and reduce costs, while improving animal welfare. The project demonstrates the impact of collaboration between tech innovators and the agricultural sector. As these technologies continue to evolve, projects like this could shape the future of farming, making it smarter and more sustainable.