Automated 3D reconstruction of grape cluster architecture from sensor data for efficient phenotyping

We propose an approach to fully-automated and sensor-based 3D reconstruction of grape cluster architecture followed by a precise, objective, and reproducible derivation of phenotypic traits. Current approaches to sensor-based phenotyping often show interactive processing steps and analyze only those parts of a plant that can be sensed by the given sensor system. Our approach employs an explicit component-based model of the architecture of grape clusters, i.e., the interconnectivity of a grape cluster's components, the geometry of the components, and the structural and geometrical constraints of their mutual connections. Based on this model, our approach can derive in a fully automated way complete 3D reconstructions of sensed grape clusters even for cases of partial occlusions in the process of sensor data acquisition. Given a complete 3D reconstruction of a grape cluster, we can derive on the one hand well known phenotypic traits of grape clusters. On the other hand, this approach facilitates measuring and evaluating new phenotypic traits. Therefore, our approach is of interest for monitoring and yield estimations in vineyards as well as for grapevine breeders. We developed and implemented our approach within a grapevine phenotyping project. First evaluations of reconstruction results and derived phenotypic traits show a potential of this approach for automated high-throughput phenotyping. We discuss the opportunities to apply our approach to other plants and with other sensor systems. (C) 2015 Elsevier B.V. All rights reserved.