Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University’s Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management.

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Over the decades in the US, the introduction of rootstocks with precocity, stress tolerance, and dwarfing has increased significantly to improve the advancement in modern orchard systems for high production of tree fruits. In pear, it is difficult to establish modern high-density orchard systems due to the lack of appropriate vigor-controlling rootstocks. The measurement of traits using unmanned aerial vehicle (UAV) sensing techniques can help in identifying rootstocks suitable for higher-density plantings. The overall goal of this study is to optimize UAV flight parameters (sensor angles and direction) and preprocessing approaches to identify ideal flying parameters for data extraction and achieving maximum accuracy. In this study, five UAV missions were conducted to acquire high-resolution RGB imagery at different sensor inclination angles (90°, 65°, and 45°) and directions (forward and backward) from the pear rootstock breeding plot located at a research orchard belonging to the Washington State University (WSU) Tree Fruit Research and Extension Center in Wenatchee, WA, USA. The study evaluated the tree height and canopy volume extracted from four different integrated datasets and validated the accuracy with the ground reference data (n = 504). The results indicated that the 3D point cloud precisely measured the traits (0.89 < r < 0.92) compared to 2D datasets (0.51 < r < 0.75), especially with 95th percentile height measure. The integration of data acquired at different angles could be used to estimate the tree height and canopy volume. The integration of sensor angles during UAV flight is therefore critical for improving the accuracy of extracting architecture to account for varying tree characteristics and orchard settings and may be useful to further precision orchard management.