As precision livestock farming continues to gain momentum, researchers at Texas A&M AgriLife are advancing artificial intelligence (AI) and sensor technology to improve dairy cow health and optimize productivity on farms of all sizes.
Leading these efforts is Dr. Sushil Paudyal, assistant professor of dairy science in the Department of Animal Science at Texas A&M University. His research focuses on the development of AI-powered, noninvasive systems to detect disease early, enhance animal welfare, and improve operational efficiency in robotic and conventional dairy settings.
Real-Time Disease Detection Through Technology
Paudyal’s lab has successfully created machine learning models capable of detecting common health issues such as lameness, mastitis, heat stress, and digital dermatitis in individual cows. These systems rely on data collected through camera images and behavioral monitoring, eliminating the need for physical sampling.
By leveraging AI and real-time analytics, his team is building diagnostic tools that track physiological and behavioral indicators to flag health concerns before they escalate. Many of these innovations use cost-effective, camera-based systems to help reduce barriers to adoption, especially for mid-size and smaller farms.
Research Highlights from the U.S. Precision Livestock Farming Conference
At the U.S. Precision Livestock Farming Conference in Lincoln, Nebraska, Paudyal and his graduate students presented several projects designed to improve robotic milking efficiency and health monitoring in dairy cattle:
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Heat Stress and Robotic Milking: Using computer vision, researchers found that heat stress negatively affects cow flow, milk yield, and robot efficiency. The results suggest improved ventilation, cooling, and feeding protocols can help mitigate performance losses during hot weather.
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Automated Mastitis and Heat Stress Monitoring: AI-enabled video monitoring systems were developed to detect signs of mastitis and heat stress using real-time behavior analysis, providing a scalable alternative to manual health checks.
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Digital Dermatitis Detection: Early prediction and classification of digital dermatitis were achieved using computer vision tools. The system identifies hoof lesions before they become severe, reducing reliance on visual scoring by farm staff.
Introducing the “DairyBot” Virtual Assistant
Looking ahead, Paudyal’s team is developing a generative AI assistant called DairyBot. This virtual tool will allow farmers to analyze herd data, assess feed strategies, and consult digital dairy literature for decision-making support. DairyBot will not replace veterinarians or nutritionists, but it aims to enhance their recommendations by providing instant, data-driven insights.
A working prototype of DairyBot is expected within six months, with early results being presented at the American Dairy Science Association conference in Louisville, Kentucky from June 22–25.
Scalable Solutions for All Dairy Farms
A major goal of Paudyal’s work is to develop adaptable, cost-effective technology that meets the needs of a wide range of dairy operations. While adoption of AI and automation tools is growing, the up-front costs can be a barrier. By designing systems that monitor group behavior without individual sensors, Paudyal hopes to make precision livestock tools more accessible.
With challenges like labor shortages, rising production costs, and climate stressors affecting the industry, data-informed management strategies are becoming essential. Through research that blends science and on-farm practicality, Texas A&M aims to equip producers with the tools to thrive in a rapidly evolving landscape.
