InfraPhenoGrid: A scientific workflow infrastructure for Plant Phenomics on the Grid

International audiencePlant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users

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

International audiencePlant 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

Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies

International audienceMulti-genotype canopies are frequent in phenotyping experiments and are of increasing interest in agriculture. Radiation interception efficiency (RIE) and radiation use efficiency (RUE) have low heritabilities in such canopies. We propose a revised Monteith equation that identifies environmental and genetic components of RIE and RUE. An environmental term, a component of RIE, characterizes the effect of the presence or absence of neighbours on light interception. The ability of a given plant to compete with its neighbours is then identified, which accounts for the genetic variability of RIE of plants having similar leaf areas. This method was used in three experiments in a phenotyping platform with 765 plants of 255 maize hybrids. As expected, the heritability of the environmental term was near zero, whereas that of the competitiveness term increased with phenological stage, resulting in the identification of quantitative trait loci. In the same way, RUE was dissected as an effect of intercepted light and a genetic term. This approach was used for predicting the behaviour of individual genotypes in virtual multi-genotype canopies. A large effect of competitiveness was observed in multi-genotype but not in single-genotype canopies, resulting in a bias for genotype comparisons in breeding fields

What structural plant modelling and image-based phenotyping can learn from each other?

International audienceIntroduction - High throughput phenotyping technologies have spread rapidly in the recent years to meet the demand for phenotyping of large panels of plants, covering a large genetic diversity and a large range of environmental conditions. Image-based technology, which allows following the architectural development of plant over time, is among the most popular, due to its simplicity, to a high degree of automation of the acquisition process, and to the richness of the information acquired. The automation of the analysis process is also actively developing (Ubbens et al., 2020), which offers unprecedentedly large and detailed dataset for plant modelling and for the development of new applications. Linking phenomics and crop modelling allows for example already to integrate the genetic variability of responses of plants to the environment, and to reason which combination of alleles is desirable for different pedo-climatic conditions, for present and future climate (Tardieu et al, 2017). By design, crop models however do not capture in details the architectural development of plants, that is the core data produced by image based phenomics. Using and adapting structural (functional) plant models for the analysis of such data will potentially minimise the loss of information, improve the modelling at fine scale and provide simulation tools that can be used as new source of information for crop modelling. Our objective is to experiment such a coupling for maize architectural development, and discuss how it may affect modelling and phenotyping. Materials and Methods - Multi-view images from a large phenotyping experiment (1600 plants, 40 days) performed on the PhenoArch platform https://www6.montpellier.inra.fr/lepse/M3P/PHENOARCH) are analysed with the Phenomenal image analysis pipeline (Artzet et al., 2019), which generates, for each plant, a sequence of 3D reconstructions at different stages of development (Figure 1A). Phenomenal also allow to segment the plant into smaller components (stem and individual leaves), and extract phenotypic feature such as leaf length, leaf width and leaf angles. We first use these data to parameterise, one-time point at a time, a static structural model of maize (Fournier et al., 2012) (Figure 1B). The different time points are then used together to estimate a dynamic model of plant development as a function of temperature (ADEL-maize, Fournier et al., 1998) (Figure 1C). To evaluate the quality of the representation of these two nested levels of simplification, virtual plants are illuminated with a light model and compared for their interception efficiency in several conditions (isolated plants and self-similar canopies, under clear sky and overcast conditions). We also assess how the raw phenotypic features extracted by Phenomenal compare to those simulated by the dynamic model. Result and discussion - Each modeling step results in a high level of compression of the data, the highest level being between the raw plant and the static model (from 1A to 1B). The first compression is essentially linked to the simplification of the specification of the geometry (from voxels to meshes) and to the use of construction rules. The second compression is linked to the use of parametric models that capture the evolution of the objects with time, but with simplifications. In terms of light interception, all models yield similar value for self-similar canopy simulation. For isolated plants, the static model produces interception values similar to raw data, but the dynamic model can have up to 50% difference on interception in zenithal direction. This is explained by an over simplification of the leaf reorientation patterns. Fitting the dynamic model allows to improve the different measurements of plant organs (lengths, width, ..). This is explained by the compilation of the repetition of the measurements over time. The dynamic model allows to get temporal leaf tracking and to identify artifacts in the segmentations. Combining raw data and leaf tracking finally allows to extract dynamics patterns of development, including the sequence of leaf reorientation, that could be used for modelling. Conclusions - Fitting a structural plant model to elaborated phenotyping data acquired in a platform was beneficial both for model improvement and data analysis. The improved model more closely matches the interception efficiency of observations and has more robust parameterisations. Data analysis was enriched with dynamical features and benefits from averaging of repeated measurements. As a result, we obtain a fully parameterised structural model for hundreds of genotypes. Foreseen application of such a model range from multi-genotype analysis of plant development, use in interpretation of phenotyping data in the field and ideotyping

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

A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

International audienceBackground: In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1–7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses.Results: We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole‑plant side views, those best suited for detecting ear position. Images are seg‑mented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth.Conclusions: The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large‑scale genetic analyses of the control of reproductive growth to changes in environ‑mental conditions in a non‑invasive and automatized way. It is available as Open Source software in the OpenAlea platform

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

International audienceBreeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection