3D point cloud data to quantitatively characterize size and shape of shrub crops. Issue 1 (6th April 2019) - Jiang, Yu; Li, Changying; Takeda, Fumiomi; Kramer, Elizabeth A; Ashrafi, Hamid; Hunter, Jamal
Abstract: Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations ( R 2 = 0.88–0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations ( R 2 = 0.78–0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management. Crop breeding: 3D measurement of blueberry bush shape: Researchers in the United States have developed a 3D imaging technique to quantitatively measure the shape of blueberry bushes in order to evaluate their suitability for mechanical harvesting. The team, led by Changying Li of the University of Georgia, used handheld LiDAR scanners to collect 3D data from bushes in blueberry fields. Their analysis pipeline converted these data into a description of the bushes, including height, width, volume, crown size, and parameters describing the shape. While some traits matched manual measurements better than others, the analysis described bush shape sufficiently to distinguish varieties. The team also created a tool to visualize key parameters related to suitability for mechanical harvesting. This study provides a promising tool to evaluate and manage varieties of blueberries and similar crops, though further work is needed to speed up data collection.