Functional-structural Plant Models Research Articles

Unravelling drivers of local adaptation through evolutionary functional-structural plant modelling.

Local adaptation to contrasting environmental conditions along environmental gradients is a widespread phenomenon in plant populations, yet we lack a mechanistic understanding of how individual agents of selection contribute to this evolutionary process. Here, we developed a novel evolutionary functional-structural plant (E-FSP) model that recreates local adaptation of virtual plants along an environmental gradient. First, we validate the model by testing if it can reproduce two elevational ecotypes of Dianthus carthusianorum occurring in the Swiss Alps. Second, we use the E-FSP model to disentangle the relative contribution of abiotic (temperature) and biotic (competition and pollination) selection pressures to elevational adaptation in D. carthusianorum. Our results suggest that elevational adaptation in D. carthusianorum is predominantly driven by the abiotic environment. The model reproduced the qualitative differences between the elevational ecotypes in two phenological (germination and flowering time) and one morphological trait (stalk height), as well as qualitative differences in four performance variables that emerge from G × E interactions (flowering time, number of stalks, rosette area and seed production). Our approach shows how E-FSP models incorporating physiological, ecological and evolutionary mechanisms can be used in combination with experiments to examine hypotheses about patterns of adaptation observed in the field.

Optimizing radiation capture in machine-harvested cotton: A functional-structural plant modelling approach to chemical vs. manual topping strategies

Topping and planting density are key agronomic management practices to optimize cotton plant structure for machine harvesting and light capture. However, modelling the effects of these practices on canopy light utilization in the field, in order to improve cotton management, remains challenging. Functional-structural plant modelling is a computational approach to explore the effects of agronomic practices on shaping plant architecture and thus light interception. This study, for the first time, utilizes the CottonXL model to quantify the significant impact of chemical topping compared to manual topping on radiation interception on machine-harvested cotton in China at different planting densities, providing new management strategies for cotton production. A more compact plant structure is shaped by chemical topping through inhibiting leaf expansion and shortening internodes of both the main stem and fruiting branches, thereby allowing more PAR interception by middle and lower leaves. Simulation results showed that the total PAR intercepted by the canopy over entire growth season was increased by 11.3 % under chemical topping compared to manual topping. This positive effect became even more pronounced with increasing plant density. These results indicate that chemical topping could be beneficial for optimizing canopy structure, enhancing light interception and lint yield, especially at higher plant densities. The results illustrate the importance of shaping plant structure for improving radiation resource capture and exemplify the potential of optimizing topping strategy and plant density to enhance crop performance. They also demonstrate the utility of a functional-structural plant model for guiding crop management.

Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model

Abstract Performing global sensitivity analysis on functional-structural plant models (FSP models) can greatly benefit both model development and analysis by identifying the relevance of parameters for specific model outputs. Setting unimportant parameters to a fixed value decreases dimensionality of the typically large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. In this work, we apply the Elementary Effects method for dimensional models with arbitrary input types, adapting the method to models with inherent randomness. Our FSP model simulated a maize stand for 160 days of growth, considering three outputs, namely yield, peak biomass and peak leaf area index (LAI). Of 52 input parameters, 12 were identified as important for yield and peak biomass and 14 for LAI. Over 70 % of parameters were deemed unimportant for the outputs under consideration, including most parameters relating to crop architecture. Parameters governing shade avoidance response and leaf appearance rate (phyllochron) were also unimportant; variations in these physiological and developmental parameters do lead to visible changes in plant architecture but not to significant changes in yield, biomass or LAI. Some inputs identified as unimportant due to their low sensitivity index have a relatively high standard deviation of effects, with high fluctuations around a low mean, which could indicate non-linearity or interaction effects. Consequently, parameters with low sensitivity index but high standard deviation should be investigated further. Our study demonstrates that global sensitivity analysis can reveal which parameter values have the most influence on key outputs, predicting specific parameter estimates that need to be carefully characterized.

3D Reconstruction of Wheat Plants by Integrating Point Cloud Data and Virtual Design Optimization

The morphology and structure of wheat plants are intricate, containing numerous tillers, rich details, and significant cross-obscuration. Methods of effectively reconstructing three-dimensional (3D) models of wheat plants that reflects the varietal architectural differences using measured data is challenging in plant phenomics and functional–structural plant models. This paper proposes a 3D reconstruction technique for wheat plants that integrates point cloud data and virtual design optimization. The approach extracted single stem number, growth position, length, and inclination angle from the point cloud data of a wheat plant. It then built an initial 3D mesh model of the plant by integrating a wheat 3D phytomer template database with variety resolution. Diverse 3D wheat plant models were subsequently virtually designed by iteratively modifying the leaf azimuth, based on the initial model. Using the 3D point cloud of the plant as the overall constraint and setting the minimum Chamfer distance between the point cloud and the mesh model as the optimization objective, we obtained the optimal 3D model as the reconstruction result of the plant through continuous iterative calculation. The method was validated using 27 winter wheat plants, with nine varieties and three replicates each. The R2 values between the measured data and the reconstructed plants were 0.80, 0.73, 0.90, and 0.69 for plant height, crown width, plant leaf area, and coverage, respectively. Additionally, the Normalized Root Mean Squared Errors (NRMSEs) were 0.10, 0.12, 0.08, and 0.17, respectively. The Mean Absolute Percentage Errors (MAPEs) used to investigate the vertical spatial distribution between the reconstructed 3D models and the point clouds of the plants ranged from 4.95% to 17.90%. These results demonstrate that the reconstructed 3D model exhibits satisfactory consistency with the measured data, including plant phenotype and vertical spatial distribution, and accurately reflects the characteristics of plant architecture and spatial distribution for the utilized wheat cultivars. This method provides technical support for research on wheat plant phenotyping and functional–structural analysis.

Accurate and semantic 3D reconstruction of maize leaves

The rapid advances in equipment and technology for crop three-dimensional (3D) data acquisition have enabled the high-throughput and high-quality acquisition of crop 3D data. However, the challenge now lies in comprehending unstructured crop 3D data and extracting phenotypes and knowledge. Extracting semantic feature points from plant leaves and achieving high-precision 3D reconstruction is a prevalent and formidable issue in this field. In this study, we present an accurate method for semantic 3D reconstruction of maize leaves. The algorithm utilizes 3D point cloud data of maize leaves as the initial input. Subsequently, the semantic 3D reconstruction of maize leaves is achieved by means of point cloud preprocessing, initial triangular meshing, hole filling, remeshing, mesh parameterization, two-dimensional (2D) edge detection, 2D semantic point interpolation, 3D semantic point selection, mesh generation, and optimization of feature-point position. A total of 56 leaves from five different maize cultivars were reconstructed utilizing this method. The results indicate that the average distance error index dCDMP between the reconstructed mesh vertices and the input point cloud is less than 0.3 cm2 for all five cultivars, and the mean of the maximum distance error index dHDMP for each cultivar is within the range of 0.5–0.8 cm, indicating it is high-accuracy. The visualization of leaves shows good consistency between the reconstructed mesh and corresponding point cloud. Additionally, the reconstructed 3D mesh of the leaves exhibits enhanced triangular facet consistency. Each vertex can be precisely identified as a leaf vein point, leaf edge point, leaf tip point, or leaf surface point, and includes semantic data such as the position on the leaf blade, which can be directly employed for phenotype estimations. High-precision reconstruction of 3D models of crop organs that include semantic information and cultivar resolution yields significant benefits. This technology can improve the resolution and accuracy of crop morphological and structural research, and it is also crucial for crop phenomics, functional-structural plant modelling, and fusion research between plant phenotyping and crop models.

An evaluation of Goudriaan’s summary model for light interception in strip canopies, using functional-structural plant models

Abstract Dealing with heterogeneity in leaf canopies when calculating light interception per species in a mixed canopy is a challenge. Goudriaan developed a computationally simple, though conceptually sophisticated, model for light interception in strip canopies, which can be reasonably represented as ‘blocks’, such as vineyards and crop rows. This model is widely used, but there is no independent verification of the model. Hence, we developed a comparison of light interception calculations with Goudriaan’s model and with detailed spatially explicit three-dimensional functional–structural plant models (FSPM) of maize in which plant architecture can be represented explicitly. Two models were developed, one with small randomly oriented leaves in blocks, similar to Goudriaan’s assumption, which we refer to as the intermediate model (IM), and another with a realistic representation of individual plants with stems and leaves having shape, orientation and so on, referred as FSPM. In IM and FSPM, light interception was calculated using ray tracing. In Goudriaan’s model, the light extinction coefficient (k), including both its daily and seasonal average values, was generated using the FSPM. Correspondence between the three models was excellent in terms of light capture for different levels of crop height, leaf area and uniformity, with the difference less than 3.3 %. The results are strong support for the use of Goudriaan’s summary model for calculating light interception in strip canopies.

Drought responses in Coffea arabica as affected by genotype and phenophase. II – photosynthesis at leaf and plant scales

Summary The aim of this work was to compare gas exchanges from leaf to whole plant scales, in two Ethiopian accessions (‘E083’ and ‘E027’), and two bred cultivars (Iapar 59 and Catuaí 99) of Arabica coffee (Coffea arabica L.) cultivated under irrigated and rainfed conditions. Variations in gas exchanges were evaluated over four phenophases (leaf expansion – BE1 and BE2, and berry harvesting – BH1 and BH2), covering the first two production years in the coffee life cycle. We addressed the following questions: Are gas exchanges modified by water availability at leaf and/or plant scales? Do bred cultivars and wild accessions differ in their physiological responses to water availability and phenophases? Photosynthesis (A), stomatal conductance (gs), and transpiration (E) were measured on the recently fully expanded leaves at the upper canopy stratum. The functional-structural plant modelling (FSPM) was used to integrate A at whole plant photosynthesis (A”p), based on 3D virtual trees constructed under VPlants modelling platform. Despite high A values of ‘E083’ overall phenophases, a strong decline in A”p under rainfed condition was observed due to lower plant leaf area as compared to irrigated condition. Catuaí 99 and ‘E083’ were more sensitive to drought than Iapar 59 and ‘E027’, considering photosynthesis at leaf and plant scales. At the last BH2 phenophase, A, gs, E, and carboxylation efficiency were similar between irrigated and rainfed conditions for all genotypes, suggesting some acclimation of leaf gas exchange to the environment. However, A”p benefited by water management in all phenophases as plant leaf area increased. These findings revealed the need to develop methodologies for structural and functional analyses at plant scale, an important step towards the realistic responses of plants and orchards to the surrounding environment.

Correction to: A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning

Correction to: A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning

Correction to: A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning

Correction to: A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.

The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence. An initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture. The subsequent model considers the 3D phytomers of maize as intelligent agents. The aim is to maximize the ratio of sunlit leaf area, and by iteratively modifying the azimuth angle of the 3D phytomers, a 3D maize canopy model that maximizes light resource interception can be constructed. Additionally, the method incorporates a reflective approach to optimize the canopy and utilizes a mesh deformation technique for detecting and responding to leaf collisions within the canopy. Six canopy models of 2 varieties plus 3 planting densities was constructed for validation. The average R2 of the difference in azimuth angle between adjacent leaves is 0.71, with a canopy coverage error range of 7% to 17%. Another 3D maize canopy model constructed using 12 distinct density gradients demonstrates the proportion of leaves perpendicular to the row direction increases along with the density. The proportion of these leaves steadily increased after 9 × 104 plants ha-1. This study presents a 3D modeling method for the maize canopy. It is a beneficial exploration of swarm intelligence on crops and generates a new way for exploring efficient resources utilization of crop canopies.

A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning

Abstract In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.

Modeling Xylem Functionality Aspects.

Depending on the questions to be answered, water flow in the xylem can be modelled following different approaches with varying spatial and temporal resolution. When focussing on the influence of hydraulic architecture upon flow dynamics, distribution of water potentials in a tree crown or questions of vulnerability of the hydraulic system, functional-structural plant models, which link representations of morphological structure with simulated processes and with a virtual environment, can be a promising tool. Such a model will then include a network of idealized xylem segments, each representing the conducting part of a stem or branch segment, and a numerical machinery suitable for solving a system of differential equations on it reflecting the hydrodynamic laws, which are the basis of the broadly accepted cohesion-tension theory of water flow in plants. We will discuss functional-structural plant models, the simplifications that are useful for hydraulic simulations within this framework, the deduction of the used differential equations from basic physical conservation laws, and their numerical solution, as well as additional necessary models of radiation, photosynthesis, and stomatal conductance. In some supplementary notes, we are shortly addressing some related questions, for example, about root systems or about the relation between macro-scale hydraulic parameters and fine-grained (anatomical) xylem structure.

Visual Functional-Structural Plant Modeling Innovatively as a Compound Eye: Opening a New Way for Advancing the Scientific Cognition of Plant Vision.

Plant vision is an interesting interdisciplinary branch of botany and vision science, and its emerging studies have composed an epic journey of discovery. However, there are few endeavors on modeling how a plant as an integrity sees. Inspired by the similarity between those discovered laws of plant vision and the visual performance of some insect species with compound eyes, the visual functional-structural plant modeling as a compound eye is innovatively proposed. Using this adapted basic-pattern-oriented modeling, we tried to validate its feasibility in terms of the structural support, visual pathway, and functional performance. First, for a diversity of woody plants, their crowns proved to show self-similar profiles, which render the omnidirectional surfaces for structurally supporting the proposed model. Second, for many plant species, their branching proved to abide by the Pareto front, which ensures the optimality of assuming the visual pathway along the branching network. Third, in canopies the varying, but existing horizontal and vertical modes of crown shyness are detected, which in functional performance accords with the panoramic visibility of the proposed model. Overall, the feasibility of compound eye modeling is validated preliminarily, with the implication of opening a way for advancing the scientific cognition of plant vision.

Optimizing photosynthetic activity of high-wire cucumber production systems using a functional-structural plant modeling approach

Optimizing photosynthetic activity of high-wire cucumber production systems using a functional-structural plant modeling approach

Efficient Bayesian automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach.

Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional-structural plant models.

Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture.

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery.

Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.

Correlating Coffea canephora 3D architecture to plant photosynthesis at a daily scale and vegetative biomass allocation.

Coffea canephora (C. canephora) has two botanical varieties, Robusta and Conilon. Intraspecific variability was hypothesized and projected for the selection of C. canephora plants able to maintain production in the context of global climate changes. For that, architectural, C-assimilation and biomass analyses were performed on 17-month-old Robusta (clones 'A1' and '3V') and Conilon (clones '14' and '19') varieties grown in non-limiting soil, water and mineral nutrient conditions. Nondestructive coffee plant architecture coding, reconstruction and plant photosynthesis estimations were performed using a functional-structural plant modeling platform OpenAlea. 3D reconstructions and inclusion of parameters calculated and estimated from light response curves, such as dark respiration (Rd), maximum rate of carboxylation of RuBisCO and photosynthetic electron transport allowed the estimation of instantaneous and daily plant photosynthesis. The virtual orchard leaf area index was low, and light was not a limiting factor in early C. canephora development stages. Under such conditions, Robusta assimilated more CO2 at the plant and orchard scale and produced higher total biomass than Conilon. Lower plant daily photosynthesis and total biomass were correlated to higher Rd in Conilon than in Robusta. Among the architectural traits, leaf inclination, size and allometry were most highly correlated with plant assimilation and biomass. Relative allocation in leaf biomass was higher in '19' Conilon than in young Robusta plants, indicating intraspecific biomass partitioning. Similarly, variation in relative distribution of the root biomass and the root volume reflected clonal variation in soil occupation, indicating intraspecific variability in space occupation competitiveness. Coffea canephora denoted high root allocation in both Conilon and Robusta clones. However, relevant differences at subspecific levels were found, indicating the high potential of C. canephora to cope with drought events, which are expected to occur more frequently in the future, because of climate changes. The methodology developed here has the potential to be used for other crops and tree species. Highlights Functional-structural plant model was used to estimate photosynthesis on a plant and daily scales in Coffea canephora (C. canephora). Among the architectural traits, leaf shape and inclination had the most impact on photosynthesis and biomass. Under non-limiting conditions, Robusta had higher plant photosynthesis and biomass than Conilon. A higher leaf biomass allocation in Conilon clone '19' than in Robusta suggested variety-specific partitioning. Variation in the relative distribution of the root biomass indicated C. canephora intraspecific soil occupation variability.

Cropbox: a declarative crop modelling framework

AbstractWe introduce Cropbox, a novel modelling framework that supports various aspects of crop modelling in a unique yet concise style. Building a crop model can be easily riddled with technical details looking trivial at first but later becoming major obstacles that hamper the whole development or application process. This is particularly the case when implementing models from scratch without relying on an established framework. Cropbox adopts a declarative approach providing a domain-specific language to reduce technical debt and assist modellers to focus on high-level abstraction formed by relations between variables and enclosing systems, rather than tinkering with low-level implementation details. The syntax of Cropbox framework is based on the Julia programming language and is deliberately constrained to avoid unintended side effects caused by common mistakes while its architecture remains open to extension. We highlight key capabilities of the framework through case studies featuring a leaf gas-exchange model and a whole-plant simulation model. We also illustrate potential extensions for supporting functional-structural plant modelling by demonstrating a 3D root architectural model as an example.

Multi‐objective optimization of root phenotypes for nutrient capture using evolutionary algorithms

SUMMARYRoot phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three‐dimensional functional–structural plant model, SimRoot, to the Borg Multi‐Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade‐offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non‐mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi‐objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.