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.

International audienceContext :Forest scientists build models to simulate stand growth and forests dynamics. Dedicated computer tools are often developed to implement these models in order to run silvicultural scenarios and explore simulation results. Aims Our objective was to encourage software reuse and simplify model implementation. Methods The scheme was to develop a framework and methodology allowing to simplify the implementation, integration, simulation and comparison of forest models by providing a set of common and standard tools. Results Capsis provides an open and modular software architecture based on various components, allowing to run forest growth simulations and display the results. The benefits of this framework are shown with the Samsara2 model, an individual-based and spatialised tree model. Capsis has been used successfully in many similar projects. In addition, the Capsis methodology defines how developers, modellers and end-users may interact. Conclusion The Capsis framework facilitates collaborative and shared software development. Moreover, it is a powerful way to support scientific animation in the frame of forest science

International audiencePl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps

Large scale biodiversity monitoring is essential for sustainable development (earth stewardship). With the recent advances in computer vision, we see the emergence of more and more effective identification tools allowing to setup largescale data collection platforms such as the popular Pl@ntNet initiative that allow to reuse interaction data. Although it covers only a fraction of the world flora, this platform is already being used by more than 300K people who produce tens of thousands of validated plant observations each year. This explicitly shared and validated data is only the tip of the iceberg. The real potential relies on the millions of raw image queries submitted by the users of the mobile application for which there is no human validation. People make such requests to get information on a plant along a hike or something they find in their garden but not know anything about. Allowing the exploitation of such contents in a fully automatic way could scale up the worldwide collection of implicit plant observations by several orders of magnitude, which can complement the explicit monitoring efforts. In this paper, we first survey existing automated plant identification systems through a five-year synthesis of the Plant-CLEF benchmark and an impact study of the Pl@ntNet platform. We then focus on the implicit monitoring scenario and discuss related research challenges at the frontier of computer science and biodiversity studies. Finally, we discuss the results of a preliminary study focused on implicit monitoring of invasive species in mobile search logs. We show that the results are promising but that there is room for improvement before being able to automatically share implicit observations within international platforms.