Deep learning models have revolutionized plant modeling by automating the extraction of plant features and characteristics from images. This high-throughput data enables researchers to analyze complex plant traits, such as growth patterns and disease susceptibility, more efficiently.

Deep learning models must be trained using diverse images to develop robust and generalized representations. However, obtaining this type of data is a time-consuming and resource-intensive process. Apart from conducting experiments, it involves the meticulous collection of substantial volumes of high-quality images, which then need to be segmented and stored appropriately. Additionally, the images must be annotated, where specific information about the objects, regions, or attributes depicted within them is added to each file. This step is crucial in enabling the algorithms to comprehend and learn from the data effectively.

To overcome the scarcity of training data, researchers have explored the use of synthetic data generation, which involves creating artificial plant images that mimic real-world data. Synthetic data can help in training deep learning models more effectively by providing large and diverse datasets.

A new article published in in silico Plants by Dirk Helmrich, PhD student at Forschungszentrum Jülich and the University of Iceland, and colleagues introduces a framework called Synavis which generates synthetic plant data and connects and directly communicates with deep learning training frameworks.

A figure with an explanation of how plants are simulated in CPlantBox at the top. First, model parametrization uses parameters from direct measurements. Then, the model simulates a 2d image of a plant. Last, the plant is reconstructed with geometry to create a 3d image. At the bottom are examples of photorealistic environments rendered using Unreal Engine. These are images of a field with a rainy, morning, foggy or sunny environment.
Individual plants are simulated in CPlantBox using measured data. Their architecture is defined by topological and geometric information in CPlantBox. Unreal Engine uses this data to produce photorealistic renderings of the plants within a virtual environment and is capable of augmenting scene data.

Synavis is composed of two components: a Functional–Structural Plant Model (FSPM) and Unreal Engine.

FSPMs simulate realistic plant morphology, mimicking various plant development dynamics under specific environmental conditions. The FSPM CPlantBox is used to generate graph-like plant structural data using algorithms. A visualization module is then used to produce 3D plants from the CPlantBox data.

Then, Unreal Engine, a graphics engine capable of photorealistic rendering, is used to generate visual representations of the plants within a virtual environment. Unreal Engine possesses the capability to augment scene data, including plant position, density, age, and lighting, thereby generating a variety of image variations.