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

Author

Fournier, Christian, Artzet, Simon, Tardieu, François, Christophe Pradal, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Hochschule Geisenheim University, University of Hannover, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
architecture, model-oriented phenotyping, high throughput phenotyping, maize, development, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

International audience; Introduction - 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.

Published
2020

2. Machine and deep learning based identification of organs within LiDAR scans of tree canopies: Application to the estimation of apple production

Author

Artzet, Simon, Pallas, Benoît, Costes, Evelyne, and Boudon, Frédéric

Abstract

Introduction - Theoretically, 3D acquisition systems such as terrestrial LiDAR technology could allow capturing tree shapes at high throughput with a high precision. However, in practice, the quality of the canopy reconstruction from data acquired in the field largely depends on the weather conditions, shape of the trees and on the position and number of scans collected. In a previous study, a HT protocol using T-LIDAR technology was developed for characterising simple architectural traits at the tree scale (volume, light interception efficiency) on a large population of apple trees (Coupel-Ledru et al., 2019). Nevertheless, LIDAR point clouds generated with this HT protocol were highly noisy, limiting thus the ability to identify all individual organs within the canopy. Machine and deep learning methods seems an interesting solution as they are capable to adapt to various types of noise by learning directly from training data. As a first case of study, we aimed to automatically detect apples within apple tree point clouds. For this, we developed two automatic pipelines based on machine and deep learning methods that were applied to tree point clouds acquired from LiDAR technology or simulated from synthetic data. Materials and Methods - Our study was carried out on 281, 3 and 4-years-old, apple trees scanned in 2018 and 2019 with terrestrial LiDAR using two specific acquisition protocols. The first one, called LowRes (described in Coupel-Ledru et al., 2019) consisted in taking a scan in the middle of the row every 5 trees, in the different rows of the orchard. With this protocol applied during 1 week, 250 trees with apples were scanned. A second protocol, called HiRes, consisted in scanning more precisely 31 trees: each being scanned from both sides. For the validation, mean and total weight of apple of each tree were measured allowing to estimate the number of apples. Additionally, synthetic data were generated by simulating LiDAR scans on 239 virtual apple trees from their 4 cardinal sides with the MAppleT model (Costes et al., 2008) and the PlantGL software (Pradal et al., 2009). Our training dataset was composed of 10 point clouds of apple trees, in which points were manually labeled into two classes (unknown or apple). It was complemented by a training dataset of 100 synthetic point clouds of simulated apple trees automatically labeled. The first pipeline based on machine learning includes three steps. First, points are characterized with Fast Point Feature Histograms (FPFH) (Rusu et al., 2009). Second, based on the computed FPFH features and reflectance information from the LiDAR, a random forest model was trained to predict the class of each point. Third, points are clustered into individual apples using the DBSCAN algorithm. The second pipeline merge the two first steps by classifying points directly from their position and reflectance information using a deep learning model based on PointNet (Qi et al., 2017). Finally pipeline performance was assessed by comparing the number of apple point clusters detected in each tree to the number of apples estimated during harvest. Results and Discussion - The random forest model currently showed the best performance, with an accuracy of 0.76 at test stage. Using the same configuration, the PointNet based model performed worse with an accuracy of 0.60, certainly due to the limited size of the training data. Using the random forest model, the pipeline predicted the number of apples per tree on synthetic data with a high accuracy (linear regression coefficient c=1.03 and r²=0.79). The accuracy was lower when applied on apple trees scanned with the HiRes protocol (c=2.18 and r²=0.51) and even lower with the LowRes protocol (c=2.16 and r²=0.23). A strong effect of the LIDAR position relative to the tree and consequently of the point cloud resolution was observed. Indeed, for the trees closest to the scan positions with LowRes protocol (i.e .for a distance tree-LIDAR nearly equivalent to HiRes protocol), the prediction was more reliable (c=2.16 and r²=0.45) than for the farest trees from the scan positions (c=2.08 and r²=0.07). Conclusion - We compared two phenotyping pipelines based on machine and deep learning approaches and applied them to evaluate and virtually experience LiDAR acquisition protocols in order to improve the quality of canopy reconstruction and organ detection. Our results suggest that a tradeoff between scan resolution and accuracy of organ detection has to be considered in future protocols, depending on the objectives. This tradeoff may depend on the tree age and training systems. With limited ground truth data, our experience shows better results with machine learning approach. However, results of the deep learning model can certainly be improved with more realistic geometric models and scanning noise for the synthetic data and/or with a larger amount of annotated data from real scans.

Published
2020

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

Author

Brichet, Nicolas, Fournier, Christian, Turc, Olivier, Strauss, Olivier, Artzet, Simon, Pradal, Christophe, Welcker, Claude, Tardieu, François, Cabrera-Bosquet, Llorenç, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Scientific Data Management (ZENITH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), ANR-11-INBS-0012/11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), 'Infrastructure Biologie Santé' Phenome supported by the National Research Agency, 'Programme d’Investissements d’Avenir' (PIA) (ANR-11-INBS-0012), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
automate, maïs, Automatic, Image-assisted phenotyping, F62 - Physiologie végétale - Croissance et développement, analyse d'image, lcsh:Plant culture, Robot-assisted imaging, Image ‑assisted phenotyping, Machine learning, lcsh:SB1-1110, apprentissage machine, Automatique / Robotique, lcsh:QH301-705.5, Water deficit, Vegetal Biology, déficit hydrique, U10 - Informatique, mathématiques et statistiques, fungi, Methodology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer vision, Maize, plateforme de phénotypage, lcsh:Biology (General), Robot‑assisted imaging, H50 - Troubles divers des plantes, sense organs, U30 - Méthodes de recherche, Biologie végétale
Abstract

Background 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 segmented, 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 environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform. Electronic supplementary material The online version of this article (10.1186/s13007-017-0246-7) contains supplementary material, which is available to authorized users.

Published
2017

4. Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

Author

Artzet, Simon, Chen, Tsu Wei, Chopard, Jérôme, Brichet, Nicolas, Mielewczik, Michael, Cohen-Boulakia, Sarah, Cabrera Bosquet, Llorenç, Tardieu, Francois, Fournier, Christian, and Pradal, Christophe

Subjects
plateforme de phénotypage, Traitement des images, Vegetal Biology, Open source, Automatic pipeline, Image analysis, Scientific workflow, 3D reconstruction, libre accès, Image Processing, modèle 3d, analyse d'image, Biologie végétale, architecture de la plante, topologie
Abstract

In the era of high-throughput visual plant phenotyping, it is crucial to design fully automated and flexible workflows able to derive quantitative traits from plant images. Over the last years, several software supports the extraction of architectural features of shoot systems. Yet currently no end-to-end systems are able to extract both 3D shoot topology and geometry of plants automatically from images on large datasets and a large range of species. In particular, these software essentially deal with dicotyledons, whose architecture is comparatively easier to analyze than monocotyledons. To tackle these challenges, we designed the Phenomenal software featured with: (i) a completely automatic workflow system including data import, reconstruction of 3D plant architecture for a range of species and quantitative measurements on the reconstructed plants; (ii) an open source library for the development and comparison of new algorithms to perform 3D shoot reconstruction and (iii) an integration framework to couple workflow outputs with existing models towards model-assisted phenotyping. Phenomenal analyzes a large variety of data sets and species from images of high-throughput phenotyping platform experiments to published data obtained in different conditions and provided in a different format. Phenomenal has been validated both on manual measurements and synthetic data simulated by 3D models. It has been also tested on other published datasets to reproduce a published semi-automatic reconstruction workflow in an automatic way. Phenomenal is available as an open-source software on a public repository.

Published
2019

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

Author

Chen, Tsu-Wei, Cabrera-Bosquet, Llorenç, Alvarez Prado, Santiago, Perez, Raphaël, Artzet, Simon, Pradal, Christophe, Coupel-Ledru, Aude, Fournier, Christian, Tardieu, François, Chen, Tsu-Wei, Cabrera-Bosquet, Llorenç, Alvarez Prado, Santiago, Perez, Raphaël, Artzet, Simon, Pradal, Christophe, Coupel-Ledru, Aude, Fournier, Christian, and Tardieu, François

Abstract

Multi-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.

Published
2019

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

Author

Perez, Raphaël, Fournier, Christian, Cabrera-Bosquet, Llorenç, Artzet, Simon, Pradal, Christophe, Brichet, Nicolas, Chen, Tsu-Wei, Chapuis, Romain, Welcker, Claude, Tardieu, François, Perez, Raphaël, Fournier, Christian, Cabrera-Bosquet, Llorenç, Artzet, Simon, Pradal, Christophe, Brichet, Nicolas, Chen, Tsu-Wei, Chapuis, Romain, Welcker, Claude, and Tardieu, François

Abstract

Breeders 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 1950‐2015. 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. (i) 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, (ii) 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.

Published
2019

7. Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

Author

Artzet, Simon, primary, Chen, Tsu-Wei, additional, Chopard, Jérôme, additional, Brichet, Nicolas, additional, Mielewczik, Michael, additional, Cohen-Boulakia, Sarah, additional, Cabrera-Bosquet, Llorenç, additional, Tardieu, François, additional, Fournier, Christian, additional, and Pradal, Christophe, additional

Published
2019
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10. InfraPhenoGrid: A scientific workflow infrastructure for plant phenomics on the Grid

Author

Pradal, Christophe, Artzet, Simon, Chopard, Jérôme, Dupuis, Dimitri, Fournier, Christian, Mielewczik, Michael, Nègre, Vincent, Neveu, Pascal, Parigot, Didier, Valduriez, Patrick, Cohen-Boulakia, Sarah, Pradal, Christophe, Artzet, Simon, Chopard, Jérôme, Dupuis, Dimitri, Fournier, Christian, Mielewczik, Michael, Nègre, Vincent, Neveu, Pascal, Parigot, Didier, Valduriez, Patrick, and Cohen-Boulakia, Sarah

Abstract

Plant 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. (Résumé d'auteur)

Published
2017

11. Image workflows for high throughput phenotyping platforms

Author

Artzet, Simon, Brichet, Nicolas, Cabrera, Llorenc, Chen, Tsu-Wei, Chopard, Jerome, Mielewczik, Michael, Fournier, Christian, Pradal, Christophe, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Godin, Christophe, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

International audience; Plant 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.

Published
2016

12. High-throughput phenotyping platform reveals genetic variability and quantitative trait loci of light-related parameters in maize models

Author

Chen, Tsu-Wei, Fournier, Christian, Artzet, Simon, Brichet, Nicolas, Chopard, Jérôme, Christophe Pradal, Alvarez-Prado, Santiago, Cabrera-Bosquet, Llorenç, Welcker, C., and Tardieu, François

Abstract

Radiation interception efficiency (RIE) and radiation use efficiency (RUE) are the main driving forces of dry mass accumulation in many crop models, so parameters related to RIE and RUE, e.g. light extinction coefficient (k) and photosynthetic parameters, have strong influences on the results of simulations. In this work, we propose a new method to estimate the RIE- and RUE-related parameters in maize models by a high-throughput phenotyping platform, PHENOARCH (https://goo.gl /x3C6oN), where images of 330 maize lines were taken and used to reconstruct the 3D-structure of the plants. The 3D plants were used to construct a virtual canopy to calculate RIE based on the RATP light model. Leaf area index (LAI) was estimated by the reconstructed 3D-structure and k was calculated from RIE and LAI. Relationship between RIE and plant developmental stage was fitted to a sigmoidal function with three parameters: maximum RIE (RIEmax), maximum change of RIE (smax) and time taken to reach smax (ts). Between genotypes, significant differences in k, RIEmax, smax and ts were found and genome wide association analysis revealed 16 QTL for k, 77 for RIEmax, 1 for smax and 7 for ts. Further parameters including RUE and relative canopy photosynthetic capacity can be also estimated by our method. We conclude that 3Dstructure of plants reconstructed in a phenotyping platform can be used to discover the genetic variability of lightrelated parameters for crop models.

Published
2016

13. Genetic variability in light-related parameters of maize models revealed by linking 3D-reconstruction of plant architecture with light model and phenotyping platform

Author

Chen, Tsu-Wei, Fournier, Christian, Artzet, Simon, Brichet, Nicolas, Chopard, Jérôme, Pradal, Christophe, Alvarez Prado, Santiago, Cabrera-Bosquet, Llorenç, Welcker, Claude, Tardieu, Francois, Godin, Christophe, Leibniz Universität Hannover [Hannover] (LUH), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Leibniz Universität Hannover=Leibniz University Hannover, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
3d reconstruction, U10 - Méthodes mathématiques et statistiques, Phenotyping, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Radiation use efficiency, F50 - Anatomie et morphologie des plantes, F30 - Génétique et amélioration des plantes, F62 - Physiologie végétale : croissance et développement
Abstract

International audience; Radiation interception efficiency (RIE) and radiation use efficiency (RUE) are the main driving forces of dry mass accumulation, so parameters related to RIE and RUE, e.g. light extinction coefficient (k) and photosynthetic parameters, have strong impacts on yields. Since these parameters are strongly related to the canopy structure, architectural traits of plants are important for characterization of the genetic variability of these parameters, a critical work for modelling the interactions between yield and environmental conditions and predicting future genetic gain. In this work, we propose a new method to estimate the RIE- and RUE-related parameters of maize for the crop models by a high-throughput phenotyping platform, PHENOARCH (https://goo.gl /x3C6oN). In autumn 2014, 330 maize lines were grown under non-stressed condition with five repetitions. For each plant, one top image and 12 side images of the plant were taken every 2-3 days. These images were used to reconstruct the 3D-architecture of the plants. To estimate RIE of a maize line on day t, the 3D plants of this line were used for constructing a virtual canopy consisting of 15 plants in three rows. Based on the architecture of this virtual canopy, RIE was calculated by the RATP light model and leaf area index (LAI) was estimated by the images analyses and the reconstructed 3D-architecture. k was obtained by computing the slope between natural logarithm of light transmittance through the canopy and LAI. Relationship between RIE and plant developmental stage was fitted to a sigmoidal function with three parameters: maximum RIE (RIEmax), maximum change of RIE (smax) and time taken to reach smax (ts). Maize lines were genotyped with 354K polymorphic SNPs and genome wide association analysis (GWAS) was performed over model parameters. Between genotypes, significant differences in k, RIEmax, smax and ts were found, ranging from 0.31-0.75 (unitless), 0.43-0.98 (unitless), 0.12-0.27 (d-1) and 18.3-39.5 (d), respectively. GWAS revealed 16 QTL for k, 77 for RIEmax, 1 for smax and 7 for ts. This study is an example of model-assisted phenotyping and we conclude that using the 3D architecture of plants reconstructed by phenotyping platform has strong potential to discover the genetic variability of light-related traits. Further possibilities include the estimation of radiation use efficiency and relative canopy photosynthetic capacity.

Published
2016

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

Author

Fournier, Christian, Artzet, Simon, Chopard, Jérôme, Mielewczik, Michael, Brichet, Nicolas, Cabrera, Llorenç, Sirault, Xavier, Cohen-Boulakia, Sarah, Pradal, Christophe, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), High Resolution Plant Phenomics Centre (HRPPC), Commonwealth Scientific and Industrial Research Organisation (CSIRO), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), ANR-11-INBS-0012/11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
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.Phenomenal currently consists of 2D image analysis workflows built with standard image libraries (OpenCV, Scikit.Image), algorithms for 3D reconstruction (Figure 1), segmentation and tracking of plant organs for maize (under development), and workflows for estimation of light interception by plants during their growth. The ADEL model [4], a 3D FSPM model for maize architectural development, is used to help segmenting plant images and to automate the mapping between segmented 3D objects and plant organs defined in the model. The 3D reconstructed model is combined with meteorological data to feed a light distribution model [5] and estimate light use efficiency. In the future, we plan to scale this approach to the analysis of more complex plants (wheat, rice) 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.

Published
2015

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