2018/04/16 by Wenbo Dong, Volkan Isler, Dong, Wenbo +1 · 1 citation
Computer Science · Environmental Science · Mathematics · #Advanced Vision and Imaging #Algorithm #Artificial intelligence #Benchmark (surveying) #Cartography #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Data mining #FOS: Computer and information sciences #Focus (optics) #Geography #Global Positioning System #Mathematics #Process (computing) #Quadtree #RGB color model #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture #Robotics (cs.RO) #Tree (set theory) #Tree traversal #Volume (thermodynamics) #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.1804.05905
10 pages, 13 figures
arxiv created 2018/04/16 · openalex publication_date 2018/04/16 · arxiv updated 2018/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Measuring tree morphology for phenotyping is an essential but labor-intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for example when measuring tree volume. Recent approaches on automating the measurement process rely on LIDAR measurements coupled with high-accuracy GPS. Usually each side of a row is reconstructed independently and then merged using GPS information. Such approaches have two disadvantages: (1) they rely on specialized and expensive equipment, and (2) since the reconstruction process does not simultaneously use information from both sides, side reconstructions may not be accurate. We also show that standard loop closure methods do not necessarily align tree trunks well. In this paper, we present a novel vision system that employs only an RGB-D camera to estimate morphological parameters. A semantics-based mapping algorithm merges the two-sides 3D models of tree rows, where integrated semantic information is obtained and refined by robust fitting algorithms. We focus on measuring tree height, canopy volume and trunk diameter from the optimized 3D model. Experiments conducted in real orchards quantitatively demonstrate the accuracy of our method.