Roqueto dos Reis, Barbara2023-09-022023-09-022023-09-01vt_gsexam:38307http://hdl.handle.net/10919/116192Ruminants play an essential role in supplying nutrients to the global population. Despite notable advancements in the livestock industry, there is a rising demand for animal protein products and a pressing need for sustainable practices. Consequently, it is imperative to focus on improving efficiency and sustainability across the environmental, economic, and social dimensions of the livestock system. Precision livestock farming (PLF) technologies have emerged as a potential solution to enhance sustainability by integrating individual animal monitoring and automated control over animal productivity, environmental impacts, health, and welfare parameters. Although PLF holds promise for improving livestock management practices, its widespread adoption is hindered by challenges including the high costs associated with implementation, data ownership, and implementation across different environments. he overarching aim of this research was to investigate and propose solutions to the challenges that limit the extensive implementation of wearable technologies in livestock systems. The primary objective of the first study was to develop and assess the utility of an open-source, low-cost research wearable technology equipped with Bluetooth for monitoring ruminants in a confined setting. The study successfully demonstrated the functionality and cost-effectiveness of this technology and its potential for monitoring ruminants' behavior in research and practical applications. Building upon the success of the technology in intensive systems, the subsequent study focused on updating the wearable sensor for deployment in extensive systems. This was achieved by incorporating LoRa data transmission and enabling real-time monitoring of livestock location. The study effectively demonstrated the feasibility of the updated technology for real-time monitoring of livestock in extensive grazing systems. In continuation of testing the feasibility of sensors, the subsequent experiment aimed to assess the accuracy and precision of a low-cost wearable sensor photoplethysmography (PPG) sensor in monitoring heart-rate (HR) of sheep housed under high-temperature conditions. The results revealed poor accuracy and precision in detecting HR changes using the PPG sensor. Future studies should explore alternative sensor deployment methods and data analytics techniques to improve the accuracy of a PPG sensor in detecting HR in livestock animals. The follow-up study focuses on evaluating the suitability of a continuous glucose monitor (CGM) designed for humans in measuring interstitial glucose concentrations in sheep, as a potential replacement for traditional blood glucose measurements. The findings demonstrated great potential of CGM in detecting changes in glucose concentrations in sheep. However, the study`s limitations such as the small sample size, warranting further investigation with a larger sample size and potential standardization with laboratory analysis bore implementing CGMs as a replacement for traditional glucose measurement methods in research. The limited expansion of technology application in extensive livestock systems, in contrast to confined operations, can be attributed to challenges such as limited battery life and data transmission. To overcome these limitations, edge processing techniques which involve performing data processing, analytics, and decision-making closer to the data source, have been proposed as cost-effective strategy for enhancing the usability of inertial measurement unit systems (IMU) in monitoring grazing animal behavior. Therefore, the objective of the fifth study was to explore different classification techniques suitable for edge processing using an open-source IMU. Analysis of variances, logistic regression, support vector machine, and random forest were evaluated for classifying grazing, walking, standing, and lying behaviors. The random forest model achieved the highest accuracy (93%) in classifying grazing using 1-minute interval. Moreover, the algorithms were compared considering a periodic snapshot of data with intervals of 3 or 5 seconds, and interesting revealed no significant impact on algorithm accuracy on differentiating behavior of grazing cows using IMU systems. Heat stress has negative impacts on animal behavior, welfare, and productivity. While IMU systems have been used to detect behavioral changes in thermoneutral conditions, their effectiveness on heat-stressed animals remains unclear. The objective of the last study was to investigate changes in sheep behavior using a low-cost IMU and the influence of ambient temperature in the algorithms ability to classify behaviors. Eating, lying, standing and ruminating while standing and lying were classified during exposure to different ambient temperature patterns. The algorithm demonstrated acceptable accuracies in differentiating behaviors under thermoneutral conditions, but its performance was impaired when tested outside the thermal range. Future research should focus on developing algorithms that account for different environmental conditions to improve the accuracy of IMU in classifying animal behavior. Collectively, these investigations contribute to enhancing the applicability of technologies in livestock systems.ETDenIn Copyrightruminantsprecision livestock technologiesbehavior monitoringDissertation