New technology tracks cattle health and productivity

DAILY Bites

• Tokyo University of Science developed a multi-camera system to track dairy cows, improving health monitoring and farm management.

• The system uses location-based tracking for higher accuracy and less stress on cows than wearable devices.

• Tests showed 90% accuracy, with plans to automate setup and enhance disease detection.

DAILY Discussion

Researchers develop a method to track dairy cows across a barn with multi-camera systems, improving accuracy

High-quality milk remains in high demand, but managing the health of dairy cows is becoming increasingly challenging. To tackle this, researchers from Tokyo University of Science have developed an innovative location information-based technique that uses multi-camera systems to track individual cows across an entire barn.

This method enables health monitoring, early disease detection, and gestation management, making it ideal for large-scale implementation to ensure dairy farm health and ensure consistent, high-quality milk production.

As dairy farmers dwindle every year, the demand for high-quality milk remains steadfast, driving a surge in dairy farming. Although this shift improves efficiency, it makes managing the health of individual cows more challenging. Effective health management has thereby become a critical issue in the dairy industry.

Early detection of abnormalities, swift diagnosis, prevention of disease spread, and maintaining proper breeding cycles are essential for desirable and stable milk production.

While other methods are available, like using mechanical devices attached to dairy cows for health monitoring, non-intrusive and non-contact techniques may be preferred. Researchers say that these methods are less stressful for the cows, as they do not require any physical attachments, making them more suitable for everyday use on farms.

These include advanced deep learning methods, such as camera-based tracking and image analysis. This approach is based on the idea that dairy cows often exhibit unusual behaviors and movement patterns due to illness, diseases, the estrus cycle, stress, or anxiety. By tracking individual movements using cameras—such as walking patterns, visits to feeding stations, and water consumption frequency—farmers can analyze cow behavior, enabling early prediction of diseases or health issues.

The team of researchers led by Assistant Professor Yota Yamamoto from the Department of Information and Computer Technology, Faculty of Engineering, along with Kazuhiro Akizawa, Shunpei Aou, and Professor Yukinobu Taniguchi, has developed a novel location-based method using a multi-camera system to track cows across an entire barn. Their findings were made available online on December 4, 2024, and will be published in Volume 229 of Computers and Electronics in Agriculture on February 1, 2025.

The proposed method for tracking dairy cows in barns relies on location information rather than complicated image patterns. Dr. Yamamoto explains the advancements of their technique, “This is the first attempt to track dairy cows across an entire barn using multi-camera systems. 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 dairy cows as they move from one camera to another, enabling seamless tracking across multiple cameras. By carefully managing the number of cameras and their fields of view, the system can minimize the negative effects of obstacles like walls or pillars, which can cause fragmented camera overlaps in barns with complex layouts. This approach 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, this method achieved about 90 percent accuracy in tracking the cows, measured through Multi-Object Tracking Accuracy, and around 80 percent Identification F1 score for identifying each individual cow. This marks a significant improvement over conventional methods, which struggled with accuracy, especially in crowded or complex barn environments.

It also performs well in different situations, whether the cows are moving slowly or standing still, and also addressed the challenge of cows lying down by adjusting the cow height parameter to 0.9 meters, lower than a standing cow’s height. This adjustment improved tracking accuracy despite posture changes.

“This method enables optimal management and round-the-clock health monitoring of dairy cows, ensuring high-quality milk production at a reasonable price,” says 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.