Precision Agriculture (PA) has emerged as a transformative approach in modern farming, integrating advanced digital technologies to enhance productivity, resource efficiency, and sustainability. This study aims to map global research trends in Precision Agriculture using a bibliometric analysis covering the period 2010–2025. Bibliographic data were retrieved from the Scopus database and analyzed using VOSviewer to examine publication growth, citation patterns, authorship collaboration, institutional networks, country contributions, and keyword co-occurrence structures. The results indicate a significant and continuous increase in scholarly output, reflecting the growing strategic importance of PA in addressing global food security and climate challenges. China, the United States, and India emerge as leading contributors, supported by strong institutional collaborations and expanding international research networks. Thematic analysis reveals that artificial intelligence, machine learning, deep learning, UAV-based remote sensing, IoT systems, and sustainability-related topics dominate the research landscape. The findings highlight a clear shift toward data-driven agricultural systems and integrated smart farming ecosystems. This study provides a comprehensive overview of the intellectual, social, and conceptual structure of Precision Agriculture research and offers insights for future research directions, technological innovation, and policy development to support sustainable agricultural transformation
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