© 2020 American Society for Photogrammetry and Remote Sensing.In this study, we present an original unified strategy for the precise extraction of individual citrus fruit trees from single digital surface model (DSM) input data. A probabilistic method combining the circular shape information with the knowledge of the local maxima in the DSM has been used for the detection of the candidate trees. An active contour is applied within each detected region to extract the borders of the objects. Thereafter, all extracted objects are seam-lessly divided into clusters considering a new feature data set formed by (1) the properties of trees, (2) planting parameters, and (3) neighborhood relations. This original clustering stage has led to two new contributions: (1) particular objects or clustered structures having distinctive characters and rela-tionships other than the citrus objects can be identified and eliminated, and (2) the information revealed by clustering can be used to recover missing citrus objects within and/or nearby each cluster. The main finding of this research is that a successful clustering can provide valuable input for iden-tifying incorrect and missing information in terms of citrus tree extraction. The proposed strategy is validated in eight test sites selected from the northern part of Mersin province of Turkey. The results achieved are also compared with the Delistate-of-the-art methods developed for tree extraction,IP: 188.8.131.52and the success of the proposed unified strategyCopyright:is clearlyAmericanhighlighted.Society.