AI Tools Bring Data-Driven Health and Efficiency to Modern Dairy Farming

Advancing Precision Dairy Care with AI-Powered Tools

The dairy industry is increasingly integrating automation through sensors and robotics. At Texas A&M AgriLife, research is focused on helping dairy producers effectively use these technologies to optimize production and improve cattle health. Sushil Paudyal, Ph.D., assistant professor of dairy science, leads efforts applying artificial intelligence (AI) and machine learning to collect detailed, real-time farm data. His work supports earlier disease detection, better decision-making, and cost-efficient robotic adoption.

"Sensor-based systems, AI, and real-time analytics are transforming everyday decisions on dairies," Paudyal explains. "For these technologies to be effective, they must be adaptable, updatable, and customized to each farm’s needs."

Building the Future of Data-Driven Dairy

Paudyal’s lab conducts practical research to help producers manage rising heat stress and shifting labor conditions. Technology-driven models are used to detect diseases early, improve cow management, and increase farm efficiency. The lab has already developed models that identify lameness, mastitis, and heat stress in individual cows using camera images and behavioral data analyzed by advanced algorithms.

Currently, they are working on machine learning models to improve robotic milking system efficiency by identifying idle times and milking failures.

At a recent U.S. Precision Livestock Farming Conference, Paudyal’s team presented key studies, including:

• Evaluating Effects of Heat Stress on Robotic Milking Systems — This research showed that managing heat stress is essential for efficient robotic milking. Heat negatively impacts cow flow, robot use, milk yield, feed intake, and milking performance. Cows in cooler environments perform better, highlighting the need for improved cooling, ventilation, and feeding strategies.
• AI-Driven Quantification of Heat Stress and Mastitis — An AI-based video monitoring system was developed to assess heat stress and mastitis via behavioral cues, enabling scalable, real-time health monitoring.
• Using Computer Vision to Detect Digital Dermatitis — This work explores computer vision and machine learning for early, noninvasive detection of digital dermatitis, enhancing health monitoring accuracy and reducing reliance on subjective scoring.

Innovation Designed for Real-World Use

A key goal is to develop noninvasive, affordable diagnostic tools that work across different dairy setups. Some systems use cameras instead of physical sensors to monitor larger groups of cows, cutting startup costs and broadening access.

"We’re advancing sensors that detect disease without invasive blood or milk sampling," Paudyal says. "These sensors track behavior and physiological signs to identify sick cows."

The team is also developing "DairyBot," a generative AI virtual assistant. It will help producers analyze farm data and lab results, and answer questions about feed and herd management by interpreting data in real time.

"DairyBot won’t replace vets or nutritionists but will support them by providing instant, data-driven insights tailored to the farm’s specifics," Paudyal adds.

Early findings will be presented at the American Dairy Science Association conference in Louisville, Kentucky, with a working prototype expected within six months.

Right-Sized Technology for All Dairies

While technology and real-time decision-making are the future, Paudyal stresses the need for flexible, appropriately scaled solutions. Adoption varies despite many farmers seeing clear returns on investment. Camera-based systems monitoring large groups of cows can reduce costs and improve uptake, helping to narrow the digital divide.

"Our focus is on developing solutions that address real-world problems faced by dairy farmers," Paudyal says. "As a land-grant university, our mission is to provide practical, immediately useful tools that help farmers manage challenges more effectively."

For those interested in AI applications and training in agriculture and related fields, resources are available at Complete AI Training.