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. Issue 1 (29th November 2021)

• Record Type: Journal Article
• Authors: Masson, Abel Louis; Caraglio, Yves; Nicolini, Eric; Borianne, Philippe; Barczi, Jean-Francois
• Editors: Schnepf, Andrea; Long, Stephen P; Schnepf, Andrea
• Is Part Of: In silico plants. Volume 4:Issue 1(2022)
• Publication Date: 2021-11-29
• Subjects: Calibration -- Deep Learning -- FSPM -- root/shoot -- whole plant; Plant physiology -- Periodicals; Botany -- Periodicals; Botany -- Mathematical models -- Periodicals; Crop science -- Periodicals; 580
• DOI: 10.1093/insilicoplants/diab036
• Languages: English
• ISSNs: 2517-5025

Abstract

Abstract: 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 %).