Mapping a Decade of Research in Big Data and Poultry Farming: A Bibliometric Analysis (2015 – 2025)

Abstract

This bibliometric study analyses Big Data and Precision Livestock Farming (PLF) research in poultry within the context of Scopus publications from 2015 to 2024. While the rapid growth of technology suggests an increasing volume of publications in this sector, there is a notable absence of a comprehensive research landscape. This study attempts to analyse publication trends, identify key contributors, and map thematic evolution in the application of Artificial Intelligence (AI) to poultry farming. A Boolean search strategy targeting Artificial Intelligence (AI) applications and Big Data methodologies (including Machine Learning, Deep Learning, IoT, and PLF) in poultry farming was implemented in Scopus. Co-authorship, co-citation, keyword co-occurrence research network visualization, and thematic cluster identification analyses were performed using the VOS Viewer software.Research evolved through three major phases: foundational studies on IoT and sensors (2015–2018), studies focusing on machine learning and animal phenotyping (2018–2024), and studies on emerging Big Data analytics and supply chain traceability (2017–2025). The primary thematic clusters include IoT and Environmental Control, Machine Learning and Animal Phenotyping and Big Data Analytics and Supply Chain Traceability. The most active countries in research were the United States, China, and India. Importantly, there is a substantial gap between the more sophisticated levels of data analysis (Levels I and II) and the less sophisticated levels of on-farm integrated decision support systems (Levels III and IV). A significant gap in practical implementation of Artificial Intelligence has been identified despite the progress to capture and analyze data. Future research should focus on the development of AI-integrated decision support systems, data-sharing policy frameworks, improved collaboration between industry and academia, and the integration of high-throughput phenotyping and genomics. The poultry sector needs these AI-driven methods to effectively leverage Big Data, anticipating enhanced production and sustainability.