Image workflows for high throughput phenotyping platforms

International audiencePlant high-throughput phenotyping aims at capturing the genetic variability of plant responses to environmental factor 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. In the frame of the Phenome high throughput phenotyping infrastructure, we develop Phenomenal, a software collection dedicated to the analysis of high throughput phenotyping data and with models. It is based on the OpenAlea platform 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. Phenomenal currently consists of 2D image analysis workflows build with standard image libraries (OpenCV, Scikit Image), algorithms for 3D reconstruction, segmentation and tracking of plant organs for maize (under development), and workflows for estimation of light interception by plants during their growth. 3D models of maize architectural development (ADEL) are used to help segmenting 3D plants and automate the mapping between topological objects detected during 3D image segmentation and plant organs defined in the model. Plant models are combined with meteorological data to feed a light distribution model (RATP) and estimate light use efficiency. In the future, we plan to scale the approach to the analysis of more complex plants (wheat) and more complex images acquired in the field. We also want to automatize the connection between plant response models and the data extracted from images, to get directly an estimation of the crop performance in a large range of context