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A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants

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 dir...

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Bibliographic Details
Published in:Scientific reports 2020-01, Vol.10 (1), p.661-661, Article 661
Main Authors: Booth, Brian G., Sijbers, Jan, De Beenhouwer, Jan
Format: Article
Language:English
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Summary: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 (
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-57405-8