Skip to main content
ARS Home » Northeast Area » Kearneysville, West Virginia » Appalachian Fruit Research Laboratory » Innovative Fruit Production, Improvement, and Protection » Research » Publications at this Location » Publication #357192
Title: 3D point cloud data to quantitatively characterize size and shape of shrub crops

Author
item JIANG, YU - University Of Georgia
item LI, CHANGYING - University Of Georgia
item Takeda, Fumiomi
item KRAMER, ELIZABETH - University Of Georgia
item ASHRAFI, HAMID - North Carolina State University
item HUNTER, JAMAL - University Of Georgia

Submitted to: Horticulture Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/5/2019
Publication Date: 4/6/2019
Citation: Jiang, Y., Li, C., Takeda, F., Kramer, E., Ashrafi, H., Hunter, J. 2019. 3D point cloud data to quantitatively characterize size and shape of shrub crops. Horticulture Research. https://doi.org/10.1038/s41438-019-0123-9.
DOI: https://doi.org/10.1038/s41438-019-0123-9

Interpretive Summary: Blueberry plant architecture influences machine harvest efficiency. Fast and accurate methods for identifying blueberry plants with certain canopy traits and small crown size would be valuable to breeding programs and for commercial growers. In this study, an active 3D imaging instrument (LiDAR) was used to provide researchers with fast 3D measurements of blueberry plants to measure bush size and shape traits. Handheld LiDAR had a dynamic and expansive sensing perspective of blueberry plants and data collected from a wide range of sensing angles dramatically reduced the possibility of missing points. The data processing pipeline used in this study accurately extracted bush size traits, especially the crown size, and provided objective evaluation and measurements for identifying blueberry plant architecture suitable for mechanical harvesting. Data can also be incorporated into agronomic management decision process, such as pruning to maintain optimal bush architecture best suited to a particular mechanical berry harvester.

Technical Abstract: Bush 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 bush size and shape traits 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 and five shape indices). One-dimensional traits (height, width, and crown size) had strong correlations (R2=0.88 to 0.95) between sensor and manual measurements; whereas, bush volume showed a decreased correlation (R2=0.78 to 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, crown size and path curve were used to form a visualization tool that helps determine 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 (especially breeding programs for mechanical harvesting) and farm management.