High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform

International audienceWe developed a non-invasive method to measure light interception and radiation-use efficiency (RUE) in thousands of maize (Zea mays) plants at the PHENOARCH phenotyping platform.Different models were interfaced to estimate (i) the amount of light reaching each plant from hemispherical images, (ii) light intercepted by each plant via a functional-structural plant model, (iii) RUE, as the ratio of plant biomass to intercepted light. The inputs of these models were leaf area, biomass and architecture estimated from plant images and environmental data collected with a precise spatial and temporal resolution. We have tested this method by comparing two experiments performed in autumn and winter/spring.Biomass and leaf area differed between experiments showing a high G×E interaction. Difference in biomass between experiments was entirely accounted for by the difference in intercepted light. Hence, the mean RUE was common to both experiments and genotypes ranked similarly.The methods presented here allowed dissecting the differences between experiments into (i) genotypic traits that did not differ between experiments but had a high genetic variability, namely plant architecture and RUE (ii) environmental differences, essentially incident light, that affected both biomass and leaf area, (iii) plant traits that differed between experiments due to environmental variables, in particular leaf growth

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

Maize whole plant image dataset

This dataset contains materials to reproduce Figure 5 that shows plant representations at different development stages (one to eight weeks after sowing) for top (a) and (b) side images, together with time courses of the number of pixels corresponding to plants extracted from side and top views (c). The following materials are available: 1. `Image dataset`: raw image dataset of side and top RGB images of a single plant that can be used in the segmentation pipeline (https://github.com/openalea/eartrack) 2. `Segmented image dataset`: output images of the segmentation pipeline 3. `Image analysis features`: A csv file contatining all image analysis features from the image dataset provided above 4. 'FIG5 dataset': small dataset of segmented images for building Figure 5a,

An image-based automated pipeline for maize ear and silk detection in a highthroughput phenotyping platform

Water deficit strongly impacts silk growth and silk emergence in maize (Zea mays L.), which in turn determines the final number of ovaries developing grains (Turc et al. 2016, Oury et al. 2016). However, phenotyping silk growth and silk expansion is difficult at throughput needed for genetic analyses. We have developed an image-based automated pipeline for maize ear and silk detection in a high-throughput phenotyping platform. The first step consists of selecting the best whole plant side images containing maximum information for each plant and day as that containing the most leaves and whole stem, based on top view images. In the second step, the best side images are segmented and skeletonized, and potential ear positions are determined based on changes in stem widths. The x, y, z ear position identified in this way serves to pilot the movement of a mobile camera able to take a detailed picture taken at 30 cm from the ear, with the final aim of determining silk emergence and silk growth duration. These methods were tested at the PhenoArch plant phenotyping platform (www6.montpellier.inra.fr/lepse/M3P) in a panel of 300 maize hybrids. First results showed that in >80% of cases, ears were successfully detected before silking and duration of silk expansion significantly correlated with visual scores. The image pipeline presented here opens up the way for large-scale genetic analyses of control of reproductive growth to changes in environmental conditions in reproductive structures

Mesures en série des échanges gazeux à l'échelle plante entière de plantes cultivées en pot

Un système permettant de mesurer la photosynthèse nette, la respiration et la transpiration individuelles de quatre plantes en pot a été développé. Ces dernières sont positionnées dans des chambres d’assimilation dont la hauteur est ajustable selon l’espèce et selon les contraintes d’encombrement liées au lieu de mesure (serre, chambre de culture, extérieur). Il s’agît d’un système ouvert : l’air dans les chambres d’assimilation est sans cesse renouvelé, ce qui évite une décroissance du taux de CO2 (due à la consommation de la plante) et un éventuel effet de serre.Le système a été testé en conditions contrôlées sur la microvigne pour différents gradients thermiques et sur le colza. Les mesures de transpiration et de photosynthèse, pour des environnements thermiques contrastés, sont conformes à celles obtenues à l’aide d’autres outils utilisés en routine en écophysiologie (balances, analyseur d’échanges gazeux à l’échelle foliaire). Les chambres permettent d’accéder à la respiration de l’ensemble des organes aériens de la plante, contrairement aux mesures d’échanges gazeux localisées sur les feuilles. Les améliorations à prévoir concernant la conception des chambres et le protocole de mesure sont discutées

PHENODYN: a high throughput platform for measurement of organ elongation rate and plant transpiration with high temporal resolution

Leaf elongation rate (LER) is the first trait affected by water deficit or high evaporative demand, with typical time constants of 30 min for change in LER upon rapid changes in soil water content or air vapour pressure deficit (VPD). The same applies to other organs such as maize silks. Phenodyn (https://www6.montpellier.inra.fr/lepse/M3P/plateforme-PHENODYN) measures organ elongation rate and transpiration rate of hundreds of plants with a temporal resolution of 3 min (or more if required) in order to follow the changes in LER and transpiration in fluctuating conditions of soil water content, evaporative demand and temperature. Phenodyn imposes known soil water potentials to each plant via independent automatic irrigation. Climatic conditions are either imposed in the growth chamber or left to naturally fluctuate in the greenhouse. Elongation rate is measured with 500 rotational displacement transducers with a precision of 0.2 mm. Transpiration and soil water content are measured with scales; changes in weight are attributed to changes in soil water content after correction for the increase in plant biomass. A set of sensors measures meristem temperature, incident light, air temperature and VPD every minute. Phenodyn is associated to an information system for real time monitoring of experiments, post-analysis of large datasets (around 700.000 data points are generated in each experiment) and identification of genotypic parameters such as rates or time constants. It has been used (i) for analyzing the response of LER to soil water potential and to VPD in mapping populations, diversity panel for association genetics or insertion lines, (ii) for establishing response curves to temperature in different species and genotypes, (iii) for following jointly changes in transpiration and in elongation rates of leaves or silks together with hydraulic variables. It has been used in maize, rice, wheat, sorghum, millet, apple tree and vine. Phenodyn is part of the M3P facility (https://www6.montpellier.inra.fr/lepse/M3P). It is accessible to public or private scientists via the website of the national project Phenome-FPPN (https://www.phenome-fppn.fr/)

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