A non-intrusive camera system designed to track dairy cow movements across barns could help farmers detect diseases early, monitor health and improve breeding management of herds.
Researchers in Japan have developed a multi-camera tracking system that tracks cows with 90% accuracy as they wander around cow sheds.
The system, which the scientists say is less intrusive and stressful than traditional health trackers that need to be attached to individual animals, has the potential to unlock new possibilities for herd management, they claim.
In a study published in Computers and Electronics in Agriculture, researchers at Tokyo University of Science explained how they used overlapping camera views to monitor cow movements across an entire barn.
By combining advanced algorithms with location-based tracking, the system can identify individual animals even in complex, crowded environments — a feat that earlier camera-based systems have struggled to achieve.
Through analysing movement patterns, visits to feeding stations and water consumption, the system can identify early signs of illness, stress or estrus, the scientists said.
This would allow farmers to intervene promptly, preventing disease outbreaks, optimising breeding cycles, and ultimately ensuring herds have better wellbeing and higher levels of productivity.
“This is the first attempt to track dairy cows across an entire barn using multi-camera systems,” said lead researcher assistant professor Yota Yamamoto.
“While previous studies have used multiple cameras to track different species of cows, each camera typically tracks cows individually, often the same cow as a different one across cameras.
“Although some methods enable consistent tracking across cameras, they have been limited to two or three cameras covering only a portion of the barn.”
The system relies on overlapping camera views to accurately and consistently track cows as they move from one camera to another, enabling seamless tracking.
By managing the number of cameras and their fields of view, the system can minimise the negative effects of obstacles like walls or pillars, which can cause fragmented camera overlaps in barns with complex layouts.
The approach also overcomes common challenges such as the cows’ speckled fur patterns and distortions caused by camera lenses, which often make traditional tracking methods less accurate.
In tests using video footage of cows moving closely together in a barn, the system achieved around 90% accuracy in tracking, and around 80% accuracy in identifying individual cows.
It also performed well in different situations, whether the cows were moving slowly or standing still, and addressed the challenge of cows lying down by adjusting the cow height parameter to less than a standing cow’s height.
“This method enables optimal management and round-the-clock health monitoring of dairy cows, ensuring high-quality milk production at a reasonable price,” said Dr. Yamamoto.
In the future, the team plans to automate the camera setup process to simplify and speed up the installation of the system in various barns.
They also aim to enhance the system’s ability to detect dairy cows that may be showing signs of illness or other health issues, helping farmers monitor and manage the health of their herds more efficiently.
