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-[Automatic Model Coupling](#automatic-model-coupling)
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-[Flexibility with Precision Control](#flexibility-with-precision-control)
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-[Batteries included](#batteries-included)
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-[Ask Questions](#ask-questions)
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-[Installation](#installation)
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-[Example usage](#example-usage)
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-[Simple example](#simple-example)
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-[Model coupling](#model-coupling)
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-[Multiscale modelling](#multiscale-modelling)
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-[Projects that use PlantSimEngine](#projects-that-use-plantsimengine)
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-[Performance](#performance)
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-[Make it yours](#make-it-yours)
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## Overview
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`PlantSimEngine` is a modelling framework for simulating and modelling plants, soil and atmosphere. It provides tools to **prototype, evaluate, test, and deploy** plant/crop models at any scale, with a strong emphasis on performance and efficiency.
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`PlantSimEngine` is a comprehensive framework for building models of the soil-plant-atmosphere continuum. It includes everything you need to **prototype, evaluate, test, and deploy** plant/crop models at any scale, with a strong emphasis on performance and efficiency, so you can focus on building and refining your models.
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**Key Features:**
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**Why choose PlantSimEngine?**
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- Process Definition: Easily define new processes such as light interception, photosynthesis, growth, soil water transfer, and more.
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- Interactive Prototyping: Fast and interactive prototyping of models with built-in constraints to avoid errors and sensible defaults to streamline the model writing process.
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- Control Degrees of Freedom: Fix variables, pass measurements, or use simpler models for specific processes to reduce complexity.
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- Automatic Management: The package automatically manages input and output variables, time-steps, objects, and the coupling of models using a dependency graph.
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- Flexible Model Switching: Switch between models without changing any code, using a simple syntax to specify the model for a given process.
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- Integrated Data Use: Force variables to take measured values instead of model predictions, reducing degrees of freedom during model development and increasing accuracy during production mode.
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- High-Performance Computation: Achieve high-speed computations, with benchmarks showing operations in the 100th of nanoseconds range for complex models (see this [benchmark script](https://github.com/VirtualPlantLab/PlantSimEngine.jl/blob/main/examples/benchmark.jl)).
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- Parallel and Distributed Computing: Out-of-the-box support for sequential, multi-threaded, or distributed computations over objects, time-steps, and independent processes, thanks to [Floops.jl](https://juliafolds.github.io/FLoops.jl/stable/).
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- Scalability: Scale easily with methods for computing over objects, time-steps, and [Multi-Scale Tree Graphs](https://github.com/VEZY/MultiScaleTreeGraph.jl).
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- Composability: Use any types as inputs, including [Unitful](https://github.com/PainterQubits/Unitful.jl) for unit propagation and [MonteCarloMeasurements.jl](https://github.com/baggepinnen/MonteCarloMeasurements.jl) for propagating measurement error.
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-**Simplicity**: Write less code, focus on your model's logic, and let the framework handle the rest.
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-**Modularity**: Each model component can be developed, tested, and improved independently. Assemble complex simulations by reusing pre-built, high-quality modules.
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-**Standardisation**: Clear, enforceable guidelines ensure that all models adhere to best practices. This built-in consistency means that once you implement a model, it works seamlessly with others in the ecosystem.
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-**Optimised Performance**: Don't re-invent the wheel. Delegating low-level tasks to PlantSimEngine guarantees that your model will benefit from every improvement in the framework. Enjoy faster prototyping, robust simulations, and efficient execution using Julia's high-performance capabilities.
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**Benefits:**
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## Unique Features
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Improved Accuracy and Reliability:
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### Automatic Model Coupling
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- Enhance the accuracy of plant growth and yield predictions by integrating detailed physiological processes and environmental interactions.
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- Reduced Modeling Time: Streamline the modeling process with automated management and fast prototyping capabilities.
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- Collaborative Research: Facilitate collaborative research efforts with flexible and composable modeling tools.
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**Seamless Integration:** PlantSimEngine leverages Julia's multiple-dispatch capabilities to automatically compute the dependency graph between models. This allows researchers to effortlessly couple models without writing complex connection code or manually managing dependencies.
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**Intuitive Multi-Scale Support:** The framework naturally handles models operating at different scales—from organelle to ecosystem—connecting them with minimal effort and maintaining consistency across scales.
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### Flexibility with Precision Control
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**Effortless Model Switching:** Researchers can switch between different component models using a simple syntax without rewriting the underlying model code. This enables rapid comparison between different hypotheses and model versions, accelerating the scientific discovery process.
-**Iterative Development**: Fast and interactive prototyping of models with built-in constraints to avoid errors and sensible defaults to streamline the model writing process.
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-**Control Your Degrees of Freedom**: Fix variables to constant values or force to observations, use simpler models for specific processes to reduce complexity.
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-**High-Speed Computations**: Achieve impressive performance with benchmarks showing operations in the 100th of nanoseconds range for complex models (see this [benchmark script](https://github.com/VirtualPlantLab/PlantSimEngine.jl/blob/main/examples/benchmark.jl)).
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-**Parallelize and Distribute Computing**: Out-of-the-box support for sequential, multi-threaded, or distributed computations over objects, time-steps, and independent processes, thanks to [Floops.jl](https://juliafolds.github.io/FLoops.jl/stable/).
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-**Scale Effortlessly**: Methods for computing over objects, time-steps, and [Multi-Scale Tree Graphs](https://github.com/VEZY/MultiScaleTreeGraph.jl).
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-**Compose Freely**: Use any types as inputs, including [Unitful](https://github.com/PainterQubits/Unitful.jl) for unit propagation and [MonteCarloMeasurements.jl](https://github.com/baggepinnen/MonteCarloMeasurements.jl) for measurement error propagation.
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## Ask Questions
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The package is designed to be easy to use, and to help users avoid errors when implementing, coupling and simulating models.
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### Simple example
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### Simple example
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Here's a simple example of a model that simulates the growth of a plant, using a simple exponential growth model:
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### Multiscale modelling
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### Multiscale modelling
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> See the [Multi-scale modeling](#multi-scale-modeling) section for more details.
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> See the Multi-scale modeling section of the docs for more details.
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The package is designed to be easily scalable, and can be used to simulate models at different scales. For example, you can simulate a model at the leaf scale, and then couple it with models at any other scale, *e.g.* internode, plant, soil, scene scales. Here's an example of a simple model that simulates plant growth using sub-models operating at different scales:
-[PlantBiophysics.jl](https://github.com/VEZY/PlantBiophysics.jl) - For the simulation of biophysical processes for plants such as photosynthesis, conductance, energy fluxes, and temperature
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-[XPalm](https://github.com/PalmStudio/XPalm.jl) - An experimental crop model for oil palm
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## Performance
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PlantSimEngine delivers impressive performance for plant modeling tasks. On an M1 MacBook Pro, a toy model for leaf area over a year at daily time-scale took only 260 μs to perform (about 688 ns per day), and 275 μs (756 ns per day) when coupled to a light interception model. These benchmarks demonstrate performance on par with compiled languages like Fortran or C, far outpacing typical interpreted language implementations.
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For example, PlantBiophysics.jl, which implements ecophysiological models using PlantSimEngine, has been measured to run up to 38,000 times faster than equivalent implementations in other scientific computing languages.
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## Make it yours
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## Make it yours
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The package is developed so anyone can easily implement plant/crop models, use it freely and as you want thanks to its MIT license.
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The package is developed so anyone can easily implement plant/crop models, use it freely and as you want thanks to its MIT license.
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If you develop such tools and it is not on the list yet, please make a PR or contact me so we can add it! 😃 Make sure to read the community guidelines before in case you're not familiar with such things.
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