Baselly Villanueva, Juan RodrigoFernández Sandoval, AndrésPinedo Freyre, Sergio FernandoSalazar Hinostroza, Evelin JudithCárdenas Rengifo, Gloria PatriciaPuerta, RonaldHuanca Diaz, José RicardoTuesta Cometivos, Gino AnthonyVallejos Torres, GeomarGoycochea Casas, GianmarcoÁlvarez Álvarez, PedroIsmail, Zool Hilmi2026-01-152026-01-152026-01-09Baselly-Villanueva, J. R., Fernández-Sandoval, A., Pinedo Freyre, S. F., Salazar-Hinostroza, E. J., Cárdenas-Rengifo, G. P., Puerta, R., Huanca Diaz, J. R., Tuesta Cometivos, G. A., Vallejos-Torres, G., Goycochea Casas, G., Álvarez-Álvarez, P., & Ismail, Z. H. (2026). UAV flight orientation and height influence on tree crown segmentation in agroforestry systems. Forests, 17(1), 87. https://doi.org/10.3390/f170100871999-4907http://hdl.handle.net/20.500.12955/2994Precise crown segmentation is essential for assessing structure, competition, and productivity in agroforestry systems, but delineation is challenging due to canopy heterogeneity and variability in aerial imagery. This study analyzes how flight height and orientation affect segmentation accuracy in an agroforestry system of the Peruvian Amazon, using RGB images acquired with a DJI Mavic Mini 3 Pro UAV and the instance-segmentation models YOLOv8 and YOLOv11. Four flight heights (40, 50, 60, and 70 m) and two orientations (parallel and transversal) were analyzed in an agroforestry system composed of Cedrelinga cateniformis (Ducke) Ducke, Calycophyllum spruceanum (Benth.) Hook.f. ex K.Schum., and Virola pavonis (A.DC.) A.C. Sm. Results showed that a flight height of 60 m provided the highest delineation accuracy (F1 ≈ 0.88 for YOLOv8 and 0.84 for YOLOv11), indicating an optimal balance between resolution and canopy coverage. Although YOLOv8 achieved the highest precision under optimal conditions, it exhibited greater variability with changes in flight geometry. In contrast, YOLOv11 showed a more stable and robust performance, with generalization gaps below 0.02, reflecting a stronger adaptability to different acquisition conditions. At the species level, vertical position and crown morphological differences (Such as symmetry, branching angle, and bifurcation level) directly influenced detection accuracy. Cedrelinga cateniformis displayed dominant and asymmetric crowns; Calycophyllum spruceanum had narrow, co-dominant crowns; and Virola pavonis exhibited symmetrical and intermediate crowns. These traits were associated with the detection and confusion patterns observed across the models, highlighting the importance of crown architecture in automated segmentation and the potential of UAVs combined with YOLO algorithms for the efficient monitoring of tropical agroforestry systems.application/pdfenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/Calycophyllum spruceanumCedrelinga cateniformisVirola pavoniscrownforest monitoringRemote sensingYOLOCoronaMonitoreo forestalTeledeteccióninfo:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#4.01.06https://doi.org/10.3390/f17010087Sistema agroforestal; Agroforestry systems; Teledetección; Remote sensing; Vehículo aéreo no tripulado; Unmanned aerial vehicles; Árbol forestal; Forest tres; Detección; Detection