Precision dairy farming technologies provide a variety of functions to dairy farmers. Little is known about dairy producer perception of these technologies. A study was performed to understand dairy producer perception of parameters monitored by precision dairy farming technologies. Calving has potential to be predicted using these same parameters and technologies. A second study was performed using two commercially marketed technologies in calving prediction. In order for these technologies to generate accurate and useful information for dairy farm use, they must accurately quantify these parameters. The final study evaluated the accuracy of five commercially marketed technologies in monitoring feeding, rumination, and lying behaviors.

Body condition scoring is a technique used to noninvasively assess fat reserves. It provides an objective estimate to describe the current and past nutritional status of the dairy cow and has been associated with increased disease risk and breeding success. Traditionally body condition scores are taken manually by visual appraisal on a 1 to 5 scale, in one-quarter increments. However, recent studies have shown the potential of automating the body condition scoring of cows using images. The first objective was to estimate the likelihood of disease development and breeding success, using odds ratios, associated with body condition score scored automatically at various points in lactation. The second objective of our research was to use a commercially available automated body condition scoring camera system to monitor body condition across the lactation period to evaluate differences between stratified parameters and to develop an equation to predict the dynamics of the body condition score. We found that poor body condition score at different times during the transition period are associated with increased disease occurrence and lower reproductive success. Automated body condition scoring (ABCS) curve during lactation was influenced by many factors, such as parity, ABCS at time of calving, disease occurrence, and milk production.

The inclusion of precision dairy farming technologies in dairy operations is an area of increasing research and industry direction. Machine vision based systems are suitable for the dairy environment as they do not inhibit workflow, are capable of continuous operation, and can be fully automated. The research of this dissertation developed and tested 3 machine vision based precision dairy farming technologies tailored to the latest generation of RGB+D cameras. The first system focused on testing various imaging approaches for the potential use of machine vision for automated dairy cow feed intake monitoring. The second system focused on monitoring the gradual change in body condition score (BCS) for 116 cows over a nearly 7 month period. Several proposed automated BCS systems have been previously developed by researchers, but none have monitored the gradual change in BCS for a duration of this magnitude. These gradual changes infer a great deal of beneficial and immediate information on the health condition of every individual cow being monitored. The third system focused on automated dairy cow feature detection using Haar cascade classifiers to detect anatomical features. These features included the tailhead, hips, and rear regions of the cow body. The features chosen were done so in order to aid machine vision applications in determining if and where a cow is present in an image or video frame. Once the cow has been detected, it must then be automatically identified in order to keep the system fully automated, which was also studied in a machine vision based approach in this research as a complimentary aspect to incorporate along with cow detection. Such systems have the potential to catch poor health conditions developing early on, aid in balancing the diet of the individual cow, and help farm management to better facilitate resources, monetary and otherwise, in an appropriate and efficient manner. Several different applications of this research are also discussed along with future directions for research, including the potential for additional automated precision dairy farming technologies, integrating many of these technologies into a unified system, and the use of alternative, potentially more robust machine vision cameras.

Poor estrus detection can limit the reproductive performance of a dairy herd. One objective of this research was to evaluate an alternative method to traditional estrus detection in the form of automated monitoring technologies. To accomplish this, the first study considered the ability of automatically monitored parameters (activity, number of steps, lying bouts, lying time, feeding time, rumination time, and temperature) to detect estrus. A second study compared automated activity monitoring to timed artificial insemination as reproductive management strategies on commercial herds. The other objective of this research was to evaluate the economic potential of automated estrus detection technologies. This was accomplished by creating and evaluating a farm specific decision support tool to determine the net present value of adopting an automated estrus detection technology.