Abstract. The development of functional-structural plant models requires an increasing amount of computer modelling. All these models are developed by different teams in various contexts and with different goals. Efficient and flexible computational frameworks are required to augment the interaction between these models, their reusability, and the possibility to compare them on identical datasets. In this paper, we present an open-source platform, OpenAlea, that provides a user-friendly environment for modellers, and advanced deployment methods. OpenAlea allows researchers to build models using a visual programming interface and provides a set of tools and models dedicated to plant modelling. Models and algorithms are embedded in OpenAlea 'components' with well defined input and output interfaces that can be easily interconnected to form more complex models and define more macroscopic components. The system architecture is based on the use of a general purpose, high-level, object-oriented script language, Python, widely used in other scientific areas. We present a brief rationale that underlies the architectural design of this system and we illustrate the use of the platform to assemble several heterogeneous model components and to rapidly prototype a complex modelling scenario.

Light interception and radiation-use efficiency (RUE) are essential components of plant performance. Their genetic dissections require novel high-throughput phenotyping methods. We have developed a suite of methods to evaluate the spatial distribution of incident light, as experienced by hundreds of plants in a glasshouse, by simulating sunbeam trajectories through glasshouse structures every day of the year; the amount of light intercepted by maize (Zea mays) plants via a functional-structural model using three-dimensional (3D) reconstructions of each plant placed in a virtual scene reproducing the canopy in the glasshouse; and RUE, as the ratio of plant biomass to intercepted light. The spatial variation of direct and diffuse incident light in the glasshouse (up to 24%) was correctly predicted at the single-plant scale. Light interception largely varied between maize lines that differed in leaf angles (nearly stable between experiments) and area (highly variable between experiments). Estimated RUEs varied between maize lines, but were similar in two experiments with contrasting incident light. They closely correlated with measured gas exchanges. The methods proposed here identified reproducible traits that might be used in further field studies, thereby opening up the way for large-scale genetic analyses of the components of plant performance.

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Summary• The emergence of a regular phyllochron from the dynamic processes of leaf initiation, leaf elongation and whorl construction suggests causal relationships between leaf elongation and leaf emergence. This paper presents a hypothesis as to how the ontogeny of the growth zone of leaves is triggered by emergence events, and implements it in a dynamic model of leaf elongation.• Two different experiments, presenting two contrasted cases of relationships between leaf emergence and kinetics of leaf elongation, were analysed and interpreted with the model in terms of the functioning of the growth zone.• Analysis of elongation kinetics revealed that the hypothesis allows for several contrasted elongation patterns that were observed, and for a regular phyllochron emerging from the variable dynamic of elongation. The model was able to simulate these patterns, and helped to identify the mechanisms underlying the key points of the analysis.• The hypothesis is not demonstrated, but its coherence and robustness are established, which should inform a renewal of the modelling of leaf elongation in architectural models.

This work initiates a modelling approach that allows us to investigate the effects of canopy architecture on foliar epidemics development. It combines a virtual plant model of wheat (Triticum aestivum L.) with an epidemic model of Septoria tritici which is caused by Mycosphaerella graminicola, a hemi-biotrophic, splashed-dispersed fungus. Our model simulates the development of the lesions from the infected lower leaves to the healthy upper leaves in the growing canopy. Epidemics result from the repeated successions of lesion development (during which spores are produced) and spores dispersal. In the model, canopy development influences epidemic development through the amount of tissue available for lesion development and through its effects on rain penetration and droplets interception during spore dispersal. Simulations show that the impact of canopy architecture on epidemic development differs between canopy traits and depends on climate. Phyllochron has the strongest effect, followed by leaf size and stem elongation rate.

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Summary• The outgrowth of tiller buds in Poaceae is influenced by the ratio of red to far-red light irradiance (R:FR). At each point in the plant canopy, R:FR is affected by light scattered by surrounding plant tissues. This paper presents a three-dimensional virtual plant modelling approach to simulate local effects of R:FR on tillering in spring wheat ( Triticum aestivum ).• R:FR dependence of bud outgrowth was implemented in a wheat model, using three hypothetical responses of bud extension to R:FR (unit step, curvilinear and linear response). Bud break occurred when a threshold bud length was reached. Simulations were performed for three plant population densities.• In accordance with experimental observations, fewer tillers per plant were simulated for higher plant population densities. The linear and curvilinear responses caused a delay in the increase in tiller number compared with experimental data. The unit step response approached experimental results best. It is suggested that a model based on relatively simple relations can be used to simulate degree of tillering.• This study has shown that the virtual plant approach is a promising tool with which to address crop morphological and ecological research questions where the determining factors act at the level of the individual plant organ.

BackgroundIn 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.ResultsWe 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.ConclusionsThe 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 materialThe online version of this article (10.1186/s13007-017-0246-7) contains supplementary material, which is available to authorized users.