Junqi Zhu¹, James Bristow², Ou An Chuang², Xiumei Yang³, Anand Rampadarath², Francisco Rojo⁴

The New Zealand Institute for Plant and Food Research Limited, ¹Marlborough, ²Auckland, ³Lincoln, ⁴Havelock 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.

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.

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.

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

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.

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.

Gerhard Buck-Sorlin¹, Michael Henke²

¹Institut Agro Rennes-Angers, France, ²Hunan 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.

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

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.

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.

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 XL (eXtended L-systems) and turtle graphics

• RGG Code structure introduction
• XL queries and operators
• XL turtle geometry

How to create a simple tomato plant and a simple tree

• A step by step growth model
• A simple tomato plant model
• Leaf triangulation

Some tips on using XL queries and XL operators

• Basic to advanced XL queries

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

• Basic spectral light modeling

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

• Getting started with using point clouds and tools
• Using a point cloud to validate a simulation
• Using a mesh cloud as a high resolution organ

Starting the GroIMP API and connecting to it through Python or R.

• Getting started with GroLink and Python(GroPy)
• Getting started with GroLink and R(GroR)
• Sensitivity Analysis using GroR
• Handling data in GroLink projects

How to create new plugins/add features to GroIM

• Create a GroIMP Plugin

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

• Setup GroIMP with git and Eclipse

Setting up distributed calculation

• Deploying GroIMP/GroLink on Kubernetes