UAV-Based High-Throughput Phenotyping to Segment Individual Apple Tree Row Based on Geometrical Features of Poles and Colored Point Cloud

Highlights Integrating RGB values and 3D coordinates of fruit trees provided phenotype-related data. The use of point clouds projection in tree rows segmentation has been confirmed. Using geometric features increased the accuracy of removing poles in an orchard. Segmenting fruit trees after removing poles on a single tree row was more accurate than traditional segmentation method. UAV equipped with LiDAR and a camera is promising for orchard high-throughput phenotyping. Abstract. High-throughput phenotyping (HTP) of fruit trees is important for providing crop geometrical information to evaluate their high yield genotypes. Unmanned aerial vehicle (UAV) is suitable for HTP by obtaining remote sensing data of large modern apple orchards, where each tree row needs to be segmented before segmenting a single tree. This study aims to develop a method for segmenting each row without noise (ERWON) of apple trees based on integrating RGB values and three-dimensional coordinates by UAV. A robust, real-time, RGB-colored, and LiDAR-inertial-visual tightly-coupled state estimation network was used to form a dense map of the orchard, which provided datasets of colored point clouds. Supporting poles were removed from the point clouds based on the consistent number of half upper parts and lower parts. Random sampling and an effective local feature aggregator were trained to segment ERWON after pole segmentation. Results showed that a precision of 0.971, a recall of 0.984, and an intersection-over-union of 0.817 for ERWON segmentation were achieved. This method proposed a potential solution for addressing the challenge of accurately and efficiently segmenting ERWON in large orchards. It is expected to be helpful for obtaining general parameters, such as geometric, morphological, and textural characteristics, as well as more specific parameters relevant to a particular phenotyping task. Keywords: Apple trees, Detection, Point cloud, RGB-colored, Segmentation.