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The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts implemented by the dairy industry. The ability of cattle to withstand heat (i.e., thermotolerance) can be influenced by physiological and behavioral factors, even though the factors contributing to thermoregulation are heritable, and cows vary in their behavioral repertoire. The current methods to gauge cow behaviors are lacking in precision and scalability. This paper presents an approach leveraging various machine learning (ML) (e.g., CNN and YOLOv8) and computer vision (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency and brush use- behaviors. These behaviors, while challenging to quantify using traditional methods, offer profound insights into the autonomic nervous system function and an individual cow’s coping mechanisms under heat stress. The developed approach provides an opportunity to quantify these difficult-to-measure drinking and brush use behaviors of dairy cows milked in a robotic milking system. This approach will open up a better opportunity for ranchers to make informed decisions that could mitigate the adverse effects of heat stress. It will also expedite data collection regarding dairy cow behavioral phenotypes. Finally, the developed system is evaluated using different performance metrics, including classification accuracy. It is found that the YoloV8 and CNN models achieved a classification accuracy of 93% and 96% for object detection and classification, respectively. 

Inspired by the human brain, Hyperdimensional Computing (HDC) processes information efficiently by operating in high-dimensional space using hypervectors. While previous works focus on optimizing pre-generated hypervectors in software, this study introduces a novel on-the-fly vector generation method in hardware with O(1) complexity, compared to the O(N) iterative search used in conventional approaches to find the best orthogonal hypervectors. Our approach leverages Hadamard binary coefficients and unary computing to simplify encoding into addition-only operations after the generation stage in ASIC, implemented using in-memory computing. The proposed design significantly improves accuracy and computational efficiency across multiple benchmark datasets.