Abstract
Precision livestock farming (PLF) is increasingly being adopted as an approach that is transforming livestock farming by addressing major challenges related to food security, improving animal health, and enhancing environmental sustainability. It also offers an opportunity to attract more young people to the sector, as technology and digital solutions spark their interest and make the profession more appealing. This article reviews the basic principles, technological advances, and future implications of PLF, with a focus on cattle, pigs, and poultry. The main technologies in precision livestock farming are: multi-layered networks of wearable sensors, advanced machine vision, and acoustic monitoring systems that provide continuous real-time data to the farmer. They are integrated with sensor networks and machine learning (ML) algorithms that provide accurate information and reliable decision-making. All of this supports the transition from population-based to individualized livestock management. The data highlight significant benefits such as improved productivity, early disease diagnosis, enhanced animal welfare, and greater sustainability through optimized resource use. Many publications report a 6–9% reduction in greenhouse gas emissions and less dependence on the prophylactic use of antimicrobials. Applications illustrate its multifunctionality, from automated detection of lameness in cattle to acoustic recognition of respiratory diseases in pigs and flock-level monitoring in poultry. Despite the promising prospects, the implementation of PLF remains limited due to high capital costs, data complexity, limited connectivity in rural areas, and challenges related to data interoperability. Ethical considerations, as well as the risks of losing direct contact between humans and animals, require careful consideration.
Keywords: Artificial Intelligence, Animal Welfare, IoT, Precision Livestock Farming, Remote Monitoring, Sustainable Agriculture