Modelling the functional dependency between root and shoot compartments to predict the impact of the environment on the architecture of the whole plant. Methodology for model fitting on simulated data using Deep Learning techniques

Autor: Jean-François Barczi, Yves Caraglio, Philippe Borianne, Abel Louis Masson, Eric Nicolini
Přispěvatelé: AgroParisTech, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
0106 biological sciences
Root (linguistics)
Plant Science
[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics
Phylogenetics and taxonomy
01 natural sciences
apprentissage machine
Mathematics
Facteur du milieu
[SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics
Modeling and Simulation
Simulated data
Shoot
Calibration
Biological system
F40 - Écologie végétale
Whole plant
F60 - Physiologie et biochimie végétale
DeepLearning
Model fitting
010603 evolutionary biology
Biochemistry
Genetics and Molecular Biology (miscellaneous)
[SDV.EE.ECO]Life Sciences [q-bio]/Ecology
environment/Ecosystems
Architecture
Croissance
Modélisation environnementale
Changement climatique
FSPM
Root/Shoot
business.industry
Deep learning
15. Life on land
Modélisation
13. Climate action
Pousse
Artificial intelligence
U30 - Méthodes de recherche
Plante de culture
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
business
Functional dependency
Agronomy and Crop Science
Racine
010606 plant biology & botany
Zdroj: in silico Plants
in silico Plants, Oxford Academic, In press, ⟨10.1093/insilicoplants/diab036⟩
in silico Plants, 2022, 4 (1), ⟨10.1093/insilicoplants/diab036⟩
In Silico Plants
Popis: Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated data sets computed from a set of more than 360 000 random TOY parameter values and random climate values. These data sets were used for training and for validation. For this purpose, we chose VoxNet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for VoxNet inputs, outputs and training. We were able to teach the network to predict the value of environment data well (mean error < 2 %), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5 % for all parameters), and for any environmental growing conditions (mean error < 20 %).
Databáze: OpenAIRE
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