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====== 12th IGUDW ======

12th International GroIMP User and Developer Workshop, December 2024.

===== FruitCropXL, a Generic Functional–Structural Fruit Crop Model to Study Tree Architecture and Fruit Quality =====
//Junqi Zhu<sup>1</sup>, James Bristow<sup>2</sup>, Ou An Chuang<sup>2</sup>, Xiumei Yang<sup>3</sup>, Anand Rampadarath<sup>2</sup>, Francisco Rojo<sup>4</sup>//

//The New Zealand Institute for Plant and Food Research Limited, <sup>1</sup>Marlborough, <sup>2</sup>Auckland, <sup>3</sup>Lincoln, <sup>4</sup>Havelock North//

Functional–structural fruit crop models that represent the 3D architecture of plants and the functionality
of different organs, can be used to guide the interventions that improve fruit productivity and quality,
enhance resource and energy use efficiency, and facilitate the use of autonomous robots. Here we present
FruitCropXL, a generic functional–structural model tailored for **Fruit Crops**, developed within GroIMP
using the eXtended L-system (**XL**).

FruitCropXL conceptualizes plants as collections of objects including buds, flowers, fruits, leaves,
internodes, fine roots, and structural roots, complemented by classes such as Phenology, Environment,
and Resource Arbitrator. FruitCropXL, originating from GrapevineXL, simulates various physiological
processes such as light interception, photosynthesis, and transpiration at the individual leaf level, along
with water uptake from different soil layers and water transport from soil to leaf based on the water
potential gradient, and solves phloem carbon concentration based on the carbon dynamics of each
organ. Furthermore, we integrated a well-recognized biophysical virtual fruit model, which enables the
simulation of various fruit types and their growth processes, including the metabolism of sugars and
acids.

Over time, we have enhanced FruitCropXL with a modularization framework that enables the toggling
between different species (e.g. grapevine and apple), module versions (simple or complex root module),
and functionalities such as light interception and carbon transport. This flexible framework allows users
to customize simulations ranging from a single organ to an entire plant, and from one hour to an entire
season, all within a single configuration file. The model is equipped to handle both static and dynamic
canopy architecture scenarios starting at any point in a growing season. It can also accommodate
different canopy and root architectures, which can be integrated either through csv files from our newly
developed shoot and root architecture generators, or directly coded architecture types within the model
itself.

  * {{ :03_community:workshops:26586_-_junqi_zhu_-_2024_groimpworkshop1.pdf |Presentation}}

===== Eco-Fuzzy Decision Model: Optimizing Agricultural Investment =====

//Ditdit Nugeraha Utama//

//Bina Nusantara University, Indonesia//


Environmental aspects are no longer merely a trend to study; they have become a critical factor to
consider in various strategic matters, including investment decision-making. The eco-decision model
(ecoDM) is a computational model designed to assist decision-makers in making objective decisions,
with one of its key parameters being the environmental (ecological) aspect. Specifically, for agricultural
investment decisions, ecoDM can be developed by utilizing data inputs supplied through plant
modeling with the functional-structural plant modeling (FSPM) approach, implemented using GroIMP.
The FSPM approach enables plant modeling to provide input data and information in various forms,
including trends, forecasts, and statistical explanations. These inputs can be further modeled to
generate actionable decision recommendations. In this context, fuzzy logic can serve as the primary
method for building the inference engine.


  * {{ :03_community:workshops:20241205_-_ecodm.pdf |Presentation}}
===== Light Quality Modelling =====

//Maarten van der Meer//

//Wageningen University %%&%% Research, The Netherlands//

Light quality is hard to measure, but very relevant for plant research as it largely determines plant
growth. With an increasing capacity to steer light quality in Controlled-Environment Agriculture (CEA) by
the minute, an understanding of how this influences plant growth is essential. Modelling with the use of
raytracing helps us to visualize and quantify light quality during crop growth. By having the numbers
quantified, we can retrospectively explain observed effects on crop growth. At the same time, better
fitting solutions and insight is provided to breeders, growers and material suppliers that work, one way
or the other, with light quality.

===== Structural Optimization of Chinese Energy-Saving Solar Greenhouses and Ideal Canopy Design for Tomato Cultivation =====

//Yue Zhang//

//Shanxi Datong University, China//

Since the 1980s, Chinese energy-saving solar greenhouses (CESGs) have evolved through three
generations, achieving significant economic and ecological benefits. However, optimizing greenhouse
structures to maximize solar energy utilization and the identification of ideal crop canopy
configurations remains critical.

A virtual simulation method for the light environment of the greenhouse and tomato canopy is
proposed, and relevant models are established. Based on this, an optimal structure screening modelling
method is created. Multiple greenhouse structure parameters are simulated and verified, and the
optimal building structure configuration for the 41.5°N latitude area in China is obtained. The new
structure can improve room temperature and light interception performance, reduce coal consumption
and increase tomato yield.

A light-thermal coupling model for simulating the microenvironment of tomato canopy leaves is also
established and verified, revealing the dynamic light and thermal microclimate of the greenhouse. The
simulation model is used to calculate the effects of various planting strategies and tomato plant
architectures, determine the ideal canopy configuration and plant architecture, analyze the quantitative
relationship between planting patterns and plant configurations, and design an ideal tomato plant
architecture, providing a reference for tomato planting and breeding

  * {{ :03_community:workshops:zhangyue_ppt.pdf |Presentation}}

===== Quantifying the Impact of Structural Model Complexity on Light Interception Simulation in Cucumber Crops Using Point Cloud Data =====

//Peige Zhong//

//Wageningen University %%&%% Research, The Netherlands//

Structural complexity plays a crucial role in the accuracy of light interception simulations for functional-
structural plant models (FSPMs). In this study, point cloud data was used to construct cucumber FSPMs
with varying levels of structural complexity. Sensitivity analyses were performed to quantify the
influence of these variations on light interception simulation outcomes. The results provide new
insights into optimizing structural model detail for accurate and efficient light interception modeling in
cucumber crops.

===== A Dwarf Tomato FSP Model for Vertical Farming =====

//Michele Butturini// 

//Wageningen University %%&%% Research, The Netherlands//

Dwarf tomato plants, characterized by compact size and determinate growth patterns, offer significant
advantages for vertical farming (VF) systems. Their reduced pruning requirements, short cultivation
cycles, and potential for automation could also revolutionize labor-intensive greenhouse practices, such
as side-shoot pruning and fruit harvesting. While these traits make dwarf tomatoes promising for VF,
their adaptation to VF-specific conditions remains limited. Breeding architectural ideotypes that
enhance light absorption and carbon assimilation is crucial for improving yield and light-use efficiency
in VF systems. A functional-structural plant (FSP) model tailored for dwarf tomatoes can facilitate this by
simulating growth dynamics and light interaction at the organ level. Using dynamic and static
simulations, the model can evaluate architectural scenarios, optimize photosynthetic efficiency, and
predict yield. Incorporating MTG (multiscale tree graph) formalism provides a robust framework for
capturing plant architecture and growth dynamics. The model’s application in VF enables the analysis of
light interception, photosynthesis, and assimilate allocation under controlled environments. This study
highlights the potential of integrating FSP modeling with experimental data to develop ideotypes that
maximize yield and resource efficiency in urban VF systems.

===== From Scrap to Craft? Using and Re-Using GroIMP Models as Serious Games for Teaching =====

//Gerhard Buck-Sorlin<sup>1</sup>, Michael Henke<sup>2</sup>//

//<sup>1</sup>Institut Agro Rennes-Angers, France, <sup>2</sup>Hunan Agriculture University, China//

In this presentation I will give an overview of the use of GroIMP as a tool for teaching, with an emphasis
on simple, "data-free" or "data-lean" models that can be used as serious games or educational toy
models for ecophysiology. Examples include (but are not limited to) the effect of mineral deficiencies on
growth and photosynthesis in tomato, or the effect of light spectrum on organ growth. Using GroIMP as
a tool for teaching plant modelling to Master students in agronomy and horticulture has been a real
challenge since I started this endeavour more than 15 years ago. This workshop contribution provides a
personal review of the various experiences and lessons drawn and attempts to sketch an outlook on
future improvements necessary to make GroIMP an (even more) attractive tool for teaching.

===== Graph-Based Point Cloud Management in GroIMP =====

//Gaëtan Heidsieck//

//University of Göttingen, Germany//

The graph-based functional structural plant modeling (FSPM) platform GroIMP was extended by a set of
functionalities for managing point clouds on the level of individual points using GroIMPs relational
graph grammar. These functionalities include the import of XYZ (simple x y z coordinates in CSV style)
and PLY based point clouds as balanced trees to the GroIMP simulation graph and a set of basic point
cloud manipulation tools. They are extended to support the possible faces and edges from the PLY as
part of the point cloud. We use this new implementation in GroIMP in two examples to showcase its
usage: a model validation and a fine-grained light interception on meshes

===== Managing Additional Graphs in GroIMP =====

//Tim Oberländer//

//University of Göttingen, Germany//

With the latest version of the Graph Explorer plugin, two new types of objects are introduced to support
the use of additional graph structures in GroIMP. These additional graphs can be used for instantiation
or cloning into other graphs, or for analysis and modification using XL, RGG or the visual editors. It is
possible to import these graphs from any format supported by GroIMP, to create new secondary graphs
on the fly or clone parts of other graphs into new graph objects.

  * {{ :03_community:workshops:additional_graphs.pdf |Presentation}}
===== Consolidating the GroIMP Plant Modelling Platform – Software Update, Plugin Management & Documentation Improvements =====

//Gaëtan Heidsieck, Tim Oberländer//

//University of Göttingen, Germany//

The GroIMP platform continues being upgraded, with newer versions of software dependencies, quality
of life improvements, additional features and more options for installation and deployment. The project
management becomes easier with some improvement in the file management, the possibility of
saving/sharing options files, and the usage of project templates. Additionally, a plugin manager ease
the usage of plugins within a GroIMP installation, which enables to benefit from the latest updates and
bug fixes. The new features implemented include: a data structure to support point clouds as graphs
within a project, which includes import/export formats as well as some basic functionalities: split,
merge, convert, and XL queries; a basic text auto completion which provides completions for modules,
classes and classes defined in a project as well as static imports: methods and classes. Finally, some
points have been raised in order to be discussed during this workshop for future improvements.

======Tutorials======

If you struggle with something in the following tutorials and you can not attend the workshop session, please feel free to reach out to the community using the FSPM forum.
===== Introduction to modelling with XL  =====

Introduction to XL (eXtended L-systems) and turtle graphics

  * [[02_user_tutorials:13_rgg:02_rgg-code-structure|RGG Code structure introduction]]
  * [[:02_user_tutorials:15_rules:01_xl-queries-and-operators| XL queries and operators ]]
  * [[:Tutorials:XL-turtle-geometry| XL turtle geometry ]]

===== Creating a simple plant in 3D =====

How to create a simple tomato plant and a simple tree

  * [[:Tutorials:architecture-model| A step by step growth model ]]
  * [[:02_user_tutorials:18_modeling:02_simple-tomato-model| A simple tomato plant model ]]
  * [[:02_user_tutorials:25_geometry:leaf-triangulation| Leaf triangulation ]]

===== Basic to advanced XL queries  =====

Some tips on using XL queries and XL operators

  * [[:Tutorials:Basic-to-advanced-xl-queries|Basic to advanced XL queries]]

===== Spectral light modelling  =====

Setting up light sources, shaders, and spectral raytracer to
calculate light distribution in the canopy

  * [[:02_user_tutorials:20_light:01_first_steps:20_basic-spectral-light-modeling| Spectral light modelling ]] 

===== Point cloud management  =====

Setting up a project with point clouds as organs in a
model. Using point cloud with XL queries

  * [[groimp-platform:pointcloudtools|Getting started with using point clouds and tools]]
  * [[:02_user_tutorials:pointclouds:05_Using-point-cloud-to-validate-model|Using a point cloud to validate a simulation]]
  * [[:02_user_tutorials:pointclouds:04_Using-mesh-clouds-as-organ|Using a mesh cloud as a high resolution organ]]


===== GroIMP API (GroLink)  =====

Starting the [[user-guide:additional_interfaces:api| GroIMP API]] and connecting to it through Python or R. 

  * [[:Tutorials:Getting-started-with-GroLink-and-GroPy|Getting started with GroLink and Python(GroPy)]]
  * [[:Tutorials:Getting-started-with-GroLink-and-GroR|Getting started with GroLink and R(GroR)]]
  * [[:Tutorials:Sensitivity-Analysis-using-GroLink-and-GroR|Sensitivity Analysis using GroR]]
  * [[:Tutorials:Handeling-data-in-GroLink-Projects|Handling data in GroLink projects]]

===== Creating/managing GroIMP plugins =====

How to create new plugins/add features to GroIM

  * [[:Tutorials:create-groimp-plugin| Create a GroIMP Plugin]]

===== Git & GroIMP =====

Setting up git/Eclipse/Maven to work with the development
of GroIMP

  * [[:Tutorials:setup-groimp-dev-environment|Setup GroIMP with git and Eclipse]]


===== GroLink and Kubernetes =====

Setting up distributed calculation

  * [[:Tutorials:GroLink-on-kubernetes| Deploying GroIMP/GroLink on Kubernetes]]