This paper presents a bibliometric analysis of UAV-based sensing for agricultural optimization, with a focus on phenotyping and crop monitoring from 2012 to 2024. Drawing on 2675 publications from 838 sources and exhibiting an annual growth rate of 41.5%, the field demonstrates rapidly expanding scholarly attention and technological innovation. Using VOSviewer and Biblioshiny, the study explores key concepts such as precision agriculture, remote sensing, advanced imaging (multispectral and hyperspectral), and machine learning algorithms. Results reveal four major thematic clusters: algorithmic and data-processing methods for phenotyping, application-oriented agriculture and sustainability concerns, UAV technology infrastructure with AI-based analytics, and spectral imaging systems for vegetation assessment. Cross-cluster linkages underscore the synergy between hardware developments, data-driven analytics, and agronomic applications. High citation rates suggest that this body of research has significant influence, shaping new insights into disease detection, yield prediction, and resource management. The findings highlight major trends, including the rise of deep learning, sensor fusion, and robotics, as well as ongoing challenges related to data standardization, validation protocols, and economic accessibility. By synthesizing these patterns, the paper offers a comprehensive overview of how UAV-based sensing is transforming large-scale phenotyping and crop monitoring, while pointing to strategic directions for future research and technological advancement.