As dairy producers adopt automation through sensors and robotics to improve production and health, researchers at Texas A&M AgriLife help them harness this evolving technology. Sushil Paudyal is an assistant professor of animal science at the Texas A&M College of Agriculture and Life Sciences Department of Animal Science. He is spearheading these efforts. He leads research that uses artificial intelligence, AI and machine learning to gather advanced data in real-time on farms. He develops systems that support earlier detection of disease, informed decisions and cost-effective adoption robotics. Paudyal stated. “But to be effective, these technologies must be adaptable, updatable and tailored to individual farm needs.”

Building a future of data-driven milk

Paudyal’s lab focuses its research on technology-based, practical research that helps producers keep up with evolving challenges such as rising heat stress and changing labour dynamics. Technology-driven models are able to detect diseases early, improve cow management and increase efficiency on dairy farms. He has already successfully deployed models that detect lameness in dairy cows as well as mastitis, heat stress and behavioral cues. He said. At the recent U.S. Paudyal’s team presented some of their research at the Precision Livestock Farming Conference (19459035) in Lincoln, Nebraska. They developed computer vision and machine learning models that determined that managing heat is crucial for robotic milking systems. It significantly affects cow flow and robot usage, milk production, feed intake, and milking performance. Cows that are kept in cooler temperatures perform better. Mitigation strategies such as improved cooling and ventilation, and adjusted feeding protocol are crucial to maintaining animal welfare and productivity.

Innovation for real-world application

Paudyal aims to create cost-effective, noninvasive diagnostic tools that can be used across diverse production systems. Some use camera-based systems instead of physical sensors to monitor large herds of cows. This reduces the initial costs and increases impact. Paudyal stated

“We are developing sensors in our lab that can help detect diseases without collecting invasive blood samples or milk samples,” . His team is developing a “They will monitor behavior and physiological variables to determine sick cows.”

virtual assistant. This tool will allow producers to evaluate farm data, lab results, and ask questions about feeding decisions, while using AI to interpret data from herds in real-time. Paudyal stated. Paudyal presented early findings “It won’t replace the vet or nutritionist, but it will empower and support them for informed decision-making.”

at the American Dairy Science Association conference in Louisville, Kentucky from June 22-25. DairyBot should have a working prototype within six months.

Right sized technology for all dairy farms

Paudyal believes that technology and real-time decisions are the future of the dairy industry. He emphasizes the importance flexible, right sized solutions. While many farmers do see a return on their investment, adoption rates can vary.

According to him, the camera-based system, which monitors larger groups of cattle, can reduce upfront costs and increase adoption. This will help minimize the digital gap. Paudyal stated. “As a land-grant university with a mission to support Texas dairy farmers, it is essential to develop research projects that provide practical, immediately applicable solutions. By equipping farmers with the tools and resources they need, we can help address real-world challenges on the farm more effectively.”