| Autor: |
Booth BG; Imec-Vision Lab, Department of Physics, University of Antwerp, B-2610, Antwerp, Belgium. brian.booth@uantwerpen.be., Sijbers J; Imec-Vision Lab, Department of Physics, University of Antwerp, B-2610, Antwerp, Belgium., De Beenhouwer J; Imec-Vision Lab, Department of Physics, University of Antwerp, B-2610, Antwerp, Belgium. |
| Jazyk: |
angličtina |
| Zdroj: |
Scientific reports [Sci Rep] 2020 Jan 20; Vol. 10 (1), pp. 661. Date of Electronic Publication: 2020 Jan 20. |
| DOI: |
10.1038/s41598-019-57405-8 |
| Abstrakt: |
In agricultural robotics, a unique challenge exists in the automated planting of bulbous plants: the estimation of the bulb's growth direction. To date, no existing work addresses this challenge. Therefore, we propose the first robotic vision framework for the estimation of a plant bulb's growth direction. The framework takes as input three x-ray images of the bulb and extracts shape, edge, and texture features from each image. These features are then fed into a machine learning regression algorithm in order to predict the 2D projection of the bulb's growth direction. Using the x-ray system's geometry, these 2D estimates are then mapped to the 3D world coordinate space, where a filtering on the estimate's variance is used to determine whether the estimate is reliable. We applied our algorithm on 27,200 x-ray simulations from T. Apeldoorn bulbs on a standard desktop workstation. Results indicate that our machine learning framework is fast enough to meet industry standards (<0.1 seconds per bulb) while providing acceptable accuracy (e.g. error < 30° in 98.40% of cases using an artificial 3-layer neural network). The high success rates of the proposed framework indicate that it is worthwhile to proceed with the development and testing of a physical prototype of a robotic bulb planting system. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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