A Bibliometric Study of Machine Learning in Precision Agriculture

Abstract

Machine Learning (ML) has revolutionized precision agriculture, offering solutions to contemporary challenges in farming practices. This paper presents a comprehensive bibliometric analysis of ML applications in precision agriculture, leveraging the Scopus database and advanced visualization tools. Through quantitative and qualitative techniques, the study interprets key trends, influential publications, and emerging research areas within this interdisciplinary field. The analysis encompasses publication and citation trends, contributing countries, influential sources, authors, collaboration networks, thematic evolution, and trending topics. The findings highlight the growing significance of ML techniques in optimizing agricultural processes, enhancing sustainability, and fostering innovation. By providing a detailed understanding of the research landscape, this study enables stakeholders to identify emerging trends, foster collaborations, and advance the application of ML in agricultural practices.