To create algorithmic simulations for analysis of how different plants and crops grow.
Project 3.5 takes a unique approach within P2IRC when studying plant growth. Przemyslaw Prusinkiewicz, Ian McQuillan, and their team of computer scientists are advancing modeling methods in which they can develop accurate simulations of plant growth. These computer simulations can create descriptive models that are visually accurate, or mechanistic models that incorporate molecular-level and other influences which can lead to a better understanding of the biological processes that control a plant’s traits. Some of these advanced modeling methods include simulation programs, tools for model exploration and analyses, and model management software.
Project Results to Date
This project has focused on furthering the VLab plant modeling software, originally developed by Przemyslaw Prusinkiewicz and his lab. This allows for the interactive creation and modification of visually rich models through the formalism of L-systems. Another focus is on collision resolution, which involves discovering methods for incorporating contact and collisions between plant organs. Also, the use of artificial data, generated from models, is being investigated as a means to aid in plant segmentation. Another component of this project is on automating simulations, which is developing computer programs that can learn models from data.
- Project 3.4 enables scientists to run tests and simulations in order to better understand the mechanisms of plant growth, or to test environmental influences. Simulations are key to the future of plant science and to the understanding of biological factors that influence plant growth and productivity.
- Developing automated simulations means that there is the potential to develop unique computer models for each plant and unique variety. This would ideally mean that scientists could scan data and transform it into custom simulations for specific scenarios.
Project 3.5 has collaborated in various ways since its inception. One of these collaborations is with Agriculture and Agri-Food Canada in creating a canola model. It has also worked closely with other Theme 3 P2IRC projects:
- Project 3.1: P2IRC Cloud: Big Data Analytics for Crop Phenotypes in deploying the VLab framework to the web in order to be used by other researchers.
- Project 3.2: Data Analysis for Rapid Plant Phenotyping in using real images and images generated from the models to train machine learning algorithms. This means that the machines are trained how to better identify specific plant features by using artificial data from the models.
Funded by NSERC:
Pascal Ferraro (Research Associate)
Charles Jason Bernard (PhD)
Andrew Owens (PhD)
Jeremy Hart (MSc)
Shannon Marie Tucker-Jones (undergrad)