Modeling 3D architecture of adult peach trees (Prunus persica (L.) Batsch) using remote sensing data

Peach fruits are a worldwide commodity used either in the fresh fruit market or in the food processing industry. However, growers need to evaluate tree traits to enable precision orchard management decisions. In this regard, a combination of remote sensing data with computational models capable of estimating canopy volume can be valuable to growers, especially for cultural practices such as spraying and pruning that can enhance yield and fruit quality. In our ongoing work, the overall objective was to obtain 3D architecture models of adult peach trees (Prunus persica (L.) Batsch) to further inferences about their canopy structure. Ground reference and remote sensing data were acquired from peach trees from a research orchard in Pullman, WA, USA. The RGB imagery was collected from a quadcopter at 15 m of altitude using three different sensor angles (45°, 65°, and 90°) and the light detection and ranging (LiDAR) data were obtained tree by tree at ground level. Canopy height model (CHM) was utilized to estimate the tree height, crown area, and volume. Correlation analysis indicated that the digital traits such tree height and volume were strongly correlated with manual measurements and LiDAR data. The integrated data (CHM developed from digital surface model combining the data acquired at different sensor angles) showed best results and may be of more practical use for growers.