The Fahlgren group at the Danforth Center uses and develops computational approaches and infrastructure that leverage large datasets to address biological problems. We emphasize the development of modular, reusable, and open-source tools through collaborator- and community-driven efforts. Our aim is to apply these tools to high-throughput genotyping and phenotyping data to identify the genetic basis of traits in research model plants and biofuel and food security crops.
The ability to rapidly and non-destructively measure plant physical and physiological features is a key bottleneck in plant research and breeding. Imaging coupled with computer vision algorithms and statistical analysis are a set of technologies that have the potential to address the plant phenotyping bottleneck, but they introduce their own computing, interpretation, and data management challenges that our group develops tools to address so that these technologies can be utilized more broadly by the scientific community. Plant Computer Vision (PlantCV) is our primary platform for developing a plant phenotyping toolbox. Through PlantCV we are deploying computer vision, machine learning, and other data science algorithms to extract biologically relevant data from image and sensor datasets.
A major emphasis of the Fahlgren group is collaboration, which enables us to apply the tools we develop to a variety of plant systems. Diverse candidate biofuel feedstocks such as Camelina sativa (oilseed) and Sorghum bicolor (lignocellulosic feedstock) are major focuses in the group where we are utilizing natural variation and high-throughput phenotyping to study the genetic basis of traits that could improve these crops for bio-based fuels. We are also developing tools for model systems (e.g. Arabidopsis thaliana and Setaria viridis), food security crops (e.g. cassava), and other systems for producing plant natural products (e.g. indigo).