Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees

Abstract

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.

Accuracy Evaluation and Branch Detection Method of 3D Modeling Using Backpack 3D Lidar SLAM and UAV-SfM for Peach Trees during the Pruning Period in Winter

“…Other studies on the 3D lidar SLAM technique discussed tilting the lidar ontology to achieve a higher density 3D point cloud model [13,[38][39][40][41]. We also tried to tilt the lidar body, but the point cloud could not be modeled smoothly, resulting in a huge error.…”

“…UAV-SfM technology uses continuous and overlapping images obtained by a camera on the UAV to construct a 3D model. This method preserves the color information of the images in the 3D model, which makes it possible to be applied in different wavelengths, such as visible light, infrared, and thermal images [8][9][10][11][12][13][14]. Nevertheless, UAV-SfM technology requires more hardware resources and computing time to complete 3D matching and modeling.…”

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

“…A number of researchers have been engaged in the adaptation of and improvement in navigation aids in horticulture. The U.S. and Brazilian researchers M. G. Raman et al (2022) [34] recognized high-resolution RGB images in a peach orchard obtained in various nadir and oblique images. The authors processed and created datasets of orthomosaics and DSM, DTM, 3D UAV and LiDAR point clouds to measure the peach tree height and crown volume.…”

Review of Photogrammetric and Lidar Applications of UAV

“…The authors have discussed UAV applications in architecture in [101][102][103][104][105][106][107]. The article [101] focused on 3D model and texture generation using a ground-based laser scanner and UAV.…”

“…Accuracy was achieved within a few tenths of a millimeter. To accurately assess the architectural features of the peach trees at Washington State University's Tukey Horticultural Orchard, both a UAV with an integrated RGB camera and a lidar system were used for reference [107]. Photogrammetric data were acquired using three flights at a flight height of 15 m AGL, with an overlap of 80%.…”