Name
Abstract | ||
|---|---|---|
Autonomous operation and information processing in an orchard environment requires an accurate inventory of the trees. Individual trees must be identified and catalogued in order to represent their distinct measures such as yield count, crop health and canopy volume. Hand labelling individual trees is a labour-intensive and time-consuming process. This paper presents a trunk localisation pipeline for identification of individual trees in an apple orchard using ground based LiDAR data. The trunk candidates are detected using a Hough Transform, and the orchard inventory is refined using a Hidden Semi-Markov Model. Such a model leverages from contextual information provided by the structured/repetitive nature of an orchard. Operating at an apple orchard near Melbourne, Australia, which hosts a modern Guttingen V trellis structure, we were able to perform tree segmentation with 89% accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-07488-7_31 | Springer Tracts in Advanced Robotics |
Field | DocType | Volume |
Orchard,Information processing,Segmentation,Simulation,Computer science,Hough transform,Lidar,Point cloud,Mixture model,Canopy | Conference | 105 |
ISSN | Citations | PageRank |
1610-7438 | 1 | 0.38 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suchet Bargoti | 1 | 58 | 3.28 |
| James Patrick Underwood | 2 | 442 | 39.37 |
| Juan I. Nieto | 3 | 939 | 88.52 |
| Salah Sukkarieh | 4 | 1142 | 141.84 |