High-precision 3D detection and reconstruction of grapes from laser range data for efficient phenotyping based on supervised learning

In this contribution, we present an automated approach to the phenotyping of grape bunches. To do so, our method analyses high-resolution sensor data taken from grape bunches and generates complete 3D reconstructions of the observed grape bunches. We extend a previous work from our group to earlier development stages with mostly visible stem structure, using an enhanced pre-classification of the sensor data into specific categories, i.e., berries and stems, yielding high precision and recall rates for the reconstruction of the berries of more than 98% and 94%, respectively. The same quality of results can be achieved by training a classification model on one grape bunch and applying it to the other grape bunches. Furtherthore, we describe important observations concerning parameter initialization and optimization techniques resulting in a guideline for people working in the area. (C) 2017 Elsevier B.V. All rights reserved.