International Journal of Molecular Zoology, 2024, Vol.14, No.6, 326-333 http://animalscipublisher.com/index.php/ijmz 331 the farm implemented training programs for staff and invested in robust sensor technologies with improved accuracy and reliability. The use of IoT platforms facilitated better data management and analysis, leading to more informed decision-making. The experience highlighted the importance of stakeholder engagement and continuous system evaluation. Table 1 Early detection of diseases; developing a model in one farm and validating it in another farm (Adopted from Halachmi et al., 2019) Farm Correct 1 83% 2 70% 3 91% 4 67% 5 77% 6.3 Broader implications for livestock health monitoring The success of the smart sensor implementation in this case study suggests a high potential for replication in other regions, particularly in developing countries where similar challenges exist. The scalability of IoT-based systems makes them suitable for various farm sizes and conditions (Iwasaki et al., 2019). The insights gained from this implementation can inform policy and regulation development by demonstrating the benefits of technology in livestock management. Policymakers can use these findings to promote the adoption of smart farming technologies and support infrastructure development (Halachmi et al., 2019). The case study provides valuable insights for future technological advancements in livestock health monitoring. It underscores the need for continuous innovation in sensor technology, data analytics, and system integration to enhance the effectiveness and efficiency of smart farming solutions (Go et al., 2022). 7 Future Directions 7.1 Emerging technologies in smart livestock monitoring The integration of internet of things (IoT) technology in livestock monitoring is transforming traditional farming practices. IoT-enabled devices facilitate remote data collection and control, enhancing precision in livestock management. These devices include sensors, actuators, and communication protocols that enable real-time monitoring of animal health and behavior (Terence et al., 2024). The use of wearable IoT devices is particularly promising, offering precise perception and sustainability monitoring, although their adaptation for farm animals is still in its nascent stages (Zhang et al., 2021). Artificial intelligence (AI) and machine learning are pivotal in processing the vast amounts of data generated by IoT devices (Chen, 2024; Huang and Lin, 2024). These technologies enable predictive analytics, allowing for early disease detection and improved decision-making in livestock management. AI-driven systems can analyze complex datasets to identify patterns and trends, thereby enhancing the efficiency and effectiveness of livestock health monitoring (Džermeikaitė et al., 2023). Bio-sensing technologies are advancing rapidly, offering new frontiers in animal health management. These include wearable sensors and biosensors that can monitor various health parameters such as body temperature, rumination, and disease biomarkers. The development of nanosensors and advanced diagnostic tools is crucial for real-time health monitoring and early disease detection in livestock. 7.2 Strategies for overcoming current challenges To make smart livestock monitoring technologies more accessible, there is a need to focus on cost-effective solutions. Innovations in IoT and wearable technologies can reduce production costs and make these tools more affordable for farmers (Church and Bork, 2023). The use of low-power wide-area networks (LPWANs) like LoRaWAN® can also help in reducing operational costs while maintaining efficient data transmission (Behjati et al., 2021). Establishing robust networks for data sharing is essential for the success of smart livestock monitoring systems. This involves developing secure and scalable IoT infrastructures that facilitate seamless data exchange
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