At the crossroads of mathematics and engineering, John Lagergren is building powerful artificial intelligence tools to dramatically advance the pursuit of hardy crops for everything from biofuels and biomaterials to natural carbon storage. The work fits well with his career goal of pursuing impactful science.
Lagergren, a staff scientist in Oak Ridge National Laboratory's Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL's Advanced Plant Phenotyping Laboratory, or APPL. Neural networks are a type of machine learning that mimic the structure of the human brain, performing tasks such as pattern recognition and decision-making when well trained.
The greenhouse-like APPL lab contains one of the most diverse suites of imaging capabilities in the world dedicated to the automated collection of measurements as plants grow, including size, biomass accumulation, photosynthetic activity, water and nitrogen content, stress response and biochemical composition. The high-resolution data collected by APPL as plants move through day and night allow scientists to quickly identify which genes underpin traits of interest and assess whether genetic modifications made to plants result in improved physical characteristics.
"Measuring all of those traits by hand is a time-consuming, miserable process that I've done myself in the field and greenhouse," Lagergren said. "What we're working toward instead is a system using AI and automation to take images and extract biologically meaningful traits for genomic analysis, and doing so in a way that is faster, more accurate, and collects data that a human can't see."
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