Temperate orchard sites have highly variable soils that contribute to uneven tree growth, providing a unique setting for precision management efforts. Precision management seeks to map spatial variability and implement variable rate management to optimize inputs. The objective of this study was to develop a platform for simultaneous measurement of tree growth and size to facilitate the enhancement of precision management practices in tart cherry (Prunus cerasus) orchards. Measurements included: canopy light interception; canopy height, volume, and width; and trunk cross sectional area (TCSA). Light interception (ceptometry) was by 15 30-cm line quantum sensors recording PAR at 0.6-second intervals, positioned on a UTV-mounted boom that passed under the canopy. Light interception was calculated based on data from a reference PAR sensor placed outside the orchard. Canopy height, width and volume of individual trees were measured using a side-scan LiDAR system mounted to the same UTV that generated point-cloud maps of each orchard row. These canopy measurements were then compared to point-cloud maps generated from aerial visual imaging (UAV). TCSA was estimated using a stereovision depth camera that captured paired visual spectrum (RGB) and point-cloud images, that were then aligned and segmented for individual tree trunks. Using pixel depth data, trunk widths for individual trees were estimated. The platform and associated sensors were used to map four commercial tart cherry orchard blocks (8-10 ha) located near Santaquin, Utah, USA, during the 2023 (ceptometry and UAV, only) and 2024 seasons at 2-week intervals. During the 2023 season, 8 ceptometry maps were generated with corresponding UAV-based maps. Side scan lidar data for 1 map of canopy height, width, and volume were collected in each of the 4 orchards, along with data for 1 TCSA map. Preliminary analysis shows strong spatial agreement among mapping techniques, indicating that the technologies are well suited to precision orchard management. Data for 2024 will also be presented, with multi-season tree growth analysis.
