The use of Big Data, Deep learning, IoT, and Blockchain in Livestock Management

Livestock management is a crucial aspect of agriculture, impacting both the economy and food security worldwide. Traditional methods of monitoring and managing livestock have often been labour-intensive and inefficient. This chapter investigates the transformative impact of Big Data, Deep Learning, Internet of Things (IoT), and Blockchain in the field of livestock management. In recent years, these technologies have revolutionized traditional agricultural practices, providing a holistic approach to ensuring the well-being and productivity of livestock. Big Data analytics play a critical role in collecting and analysing massive datasets from animal-connected sensors and wearables attached to animals, providing farmers with valuable insights into health, behaviour, and environmental conditions. Deep Learning algorithms help automate tasks. such as real-time monitoring, disease detection, and behavioural analysis, leading to proactive interventions for individual animal welfare and the overall health of the flock or herd. The integration of IoT devices creates an interconnected network, enabling the accurate monitoring of animal parameters and environmental factors, thereby optimizing feeding, breeding, and disease control. Furthermore, blockchain technology ensures transparency and traceability in the livestock supply chain, promoting trust between stakeholders and consumers by recording critical information on the origin, health, and ethical treatment of animals. This chapter presents current research and technological advances, offering a comprehensive exploration of the applications of Big Data, Deep Learning, IoT, and Blockchain in livestock management as mentioned. By examining the synergy between these technologies and their collective impact on livestock welfare, productivity, and sustainability, this work contributes to the growing body of knowledge aimed at advancing modern agricultural practices in the livestock farming sector.