Ruminants 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. / Doctor of Philosophy / The global population relies on ruminant animals, such as cattle and sheep, for essential nutrient. However, with the increasing demand for animal protein products, there is a growing need for sustainable practices in the livestock industry. Precision livestock farming (PLF) technologies have emerged as a potential solution to enhance sustainability by enabling individual animal monitoring. However, challenges such as data ownership and accessibility and high costs, impair its adoption. To overcome these challenges and enhance the applicability of wearable sensors in livestock systems, this research aimed to explore potential solutions. The objective of the first study was to develop and evaluate an open-source, low-cost wearable technology equipped with Bluetooth for monitoring ruminants in confined settings. The study successfully demonstrated the functionality and cost-effectiveness of this technology for monitoring ruminant behavior. Building up 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, enabling real-time monitoring of livestock. The study effectively demonstrated the feasibility of and potential of the updated technology for real-time monitoring in extensive livestock systems. Continuing with the feasibility testing of technologies, the next experiment aimed to assess the accuracy and precision of a low-cost photoplethysmography (PPG) sensor in monitoring heart rate (HR) in sheep housed under high-temperature conditions. Unfortunately, the results indicated poor accuracy and precision in detecting HR changes using the PPG sensor. Future studies should explore alternative sensor deployment methods and data analysis techniques to improve the accuracy of PPG sensors for HR monitoring in livestock animals. The followed study focused on evaluating the suitability of a continuous glucose monitors (CGM) designed for humans to measure interstitial glucose concentrations in sheep and potentially replacing traditional blood glucose measurements. The findings demonstrated the potential of CGMs to detect changes in glucose but limitations such as the small sample size suggest the need for further investigations with a larger sample size and potential standardization with laboratory analysis before implementing CGM as a replacement for traditional glucose measurement methods in research. In extensive systems, where technology adoption has been slower compared to confined operations, edge processing techniques are proposed as a cost-effective strategy to monitor grazing animal behavior using inertial measurement unit systems (IMU). In the fifth study, different classification techniques were explored using an open-source IMU, including analysis of variances, logistic regression, support vector machine, and random forest. The random forest model achieved high accuracy (93%) in classifying grazing behavior with a 1-minute interval. Surprisingly, algorithm accuracy was not affected when snapshot in time was performed. The final study focused on using a low-cost IMU to investigate sheep behavior under varying ambient temperature conditions. While algorithm performed well under thermoneutral conditions, its accuracy decreased outside the thermal range. Future research should focus on algorithms that account for different environmental conditions to improve IMU accuracy in classifying behavior. These investigations contribute to enhancing technology's applicability to in livestock systems by addressing challenges and developing practical solutions to improve livestock management and animal well-being.