DEVELOPMENT AND IMPLEMENTATION OF AN ECONOMICALLY VIABLE COMPUTER VISION SYSTEM TO MONITOR AND CONTROL METABOLIC DISORDERS IN DAIRY COWS

We will address the Inter-Disciplinary Engagement in Animalimplementing management tools to individually monitor transition dairy cows is the lack of integratedcow-level information among sensor technologies. Even though cutting-edge technology, such asartificial intelligence (AI) systems, has been extensivelyproposed, its cost-effectiveness, feasibility,and integration as a tool for early detection of peripartum diseases in dairy cows has not been criticallyevaluated or developed into powerful predictive analytics for optimized management decisions.Moreover, in the livestock sector, this emerging field of AI has not yet been widely translated throughextension and outreach programs to better inform and educate potential users. In this project, we willdevelopand implement a computer vision system that integrates novel phenotypes from body shapeand feeding behavior to improve animal health and welfare in livestock farms. We will alsoinvestigate both farm-level economic impacts and the complementary assessment of consumerwillingness-to-pay forproducts with improved animal health and welfare. Our goal is to createpowerful predictive modeling frameworks for accurate, real-time detection ofperipartum diseases indairy cows and to evaluate its economic and societal impact. Such goals will complement novelextension and outreach activitiesproposed to educate new generations of students andstakeholders on AI, data, and technology for livestock. The integration of novel phenotypesthroughAI technology is critical to advance farm management decisions when volatile profit margins,labor shortage, and suboptimal animal welfare aredetrimental factors constraining farmsustainability.We assembled a team of experts in dairy cattle management and welfare, economics, andcomputerscience. We will develop and deploy an integrated computer vision system to monitor healthproblems in dairy operations and an extension program toeducate farmers, stakeholders, and studentsfor the new era of digital solutions by achieving four aims:Aim 1. Develop a computer vision system that integrates animal recognition with real-timemonitoring of feeding behavior and body tissue mobilization.Aim 2. Integrate new features acquired using a computer vision system with other animal-levelinformation into predictive analytical tools forearly detection of health issues in dairycows during the transition period.Aim 3. Create a novel extension program on artificial intelligence for animal farming toeducate the next generation of students, farmers, andindustry stakeholders.Aim 4. Evaluate the economic impact of technology driven decisions and consumerwillingness-to-pay for potential improved animal healthand welfare outcomes throughuse of the proposed computer vision system.