DETECTING SUBACUTE RUMINAL ACIDOSIS USING A REAL-TIME DEEP LEARNING ALGORITHM

This research hopes to employ recent and future technologies for cattle management and production. Artificial Intelligence and CV have incredible potential to transform key areas of business and develop myriad of applications for various sectors including agriculture. In recent years, scientists have been exploring the use of AI and CV in animal farming systems to improve the efficiency of animals' production, improve animals' health and reduce their impact on the environment. This proposal has three main objectives: 1) developing multidiscipline joint-research activities to promote cattle health and production, 2) conducting educational activities to promote the incorporation of AI in cattle farming and animal science educational programs, and 3) disseminating the recent and future AI techniques to the relevant societies and agencies.Developing multidiscipline joint-research activities to promote cattle health and production:Developing a real-time, efficient, and automated CV and DL models for the early detection of SARA. We will be using the emitted gas composition (CH4 and CO2) from the rumen, using an in vitro system, as variables to develop CV and DL algorithmic models capable of identifying SARA at an early stage using an automated real-time system.Study the emitted gas composition and ratio (CH4 and CO2) under different rumen pH levels with the aim to identify the trade-point between gas composition and the development of SARA.Conducting educational activities: The advances in AI offer significant opportunities to explore how cattle production systems may benefit from these recent technologies. This project will conduct two training workshops to promote AI development skills to animal science students, agriculture teachers, and extension personals.Disseminating these technologies to agriculture societies (e.g., dairy and beef cattle livestock) and extension agencies.