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

Abstract

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.

Plant phenotyping relevance

X線画像と機械学習により球根の成長方向という植物器官の形態特性を推定する手法を開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

The article describes CT scans of 68 T. Apeldoorn bulbs, simulated x-ray projections, and MATLAB analysis code, but contains no public data or code availability statement, no repository deposit, and no authors' URL for datasets, images, models, or scripts. The supplementary information is only referenced as a PDF of (e

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