Phenomenal: a software framework for model-assisted analysis of high throughput plant phenotyping data

Abstract : Plant high-throughput phenotyping aims at capturing the genetic variability of plant response to environmental factors for thousands of plants, hence identifying heritable traits for genomic selection and predicting the genetic values of allelic combinations in different environment. This first implies the automation of the measurement of a large number of traits to characterize plant growth, plant development and plant functioning. It also requires a fluent and versatile interaction between data and continuously evolving plant response models, that are essential in the analysis of the marker x environment interaction and in the integration of processes for predicting crop performance [1]. In the frame of the Phenome high throughput phenotyping infrastructure, we develop Phenomenal: a software framework dedicated to the analysis of high throughput phenotyping data and models. It is based on the OpenAlea platform [2] that provides methods and softwares for the modelling of plants, together with a user-friendly interface for the design and execution of scientific workflows. OpenAlea is also part of the InfraPhenoGrid infrastructure that allows high throughput computation and recording of provenance during the execution [3]. Figure 1: The 3D plant reconstruction and segmentation pipeline. Muti-view plants images from PhenoArch are binarised and used to reconstruct plants in3D. The 3D skeleton is extracted and separated into stem (central vertical elements) and leaves. 3D voxels are segmented by propagating skeleton segmentation.
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Christian Fournier, Simon Artzet, Jérôme Chopard, Michael Mielewczik, Nicolas Brichet, et al.. Phenomenal: a software framework for model-assisted analysis of high throughput plant phenotyping data. IAMPS 2015 (International Workshop on Image Analysis Methods for the Plant Sciences), Sep 2015, Louvain-la-Neuve, Belgium. ⟨⟩. ⟨hal-01253627⟩



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