[Podcast] Using Machine Learning to Discover Crop Traits and Launching Benson Hill Biosystems

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Last week, Benson Hill Biosystems announced the launch of CropOS, its data analytics platform, which uses machine learning and cloud biology to improve crop performance and help scientists and breeders tackle agriculture’s biggest challenges.

CropOS is available to researchers, farmer cooperatives, breeders, and other agtech companies that want to shorten the time it takes them to identify the traits and varieties they want by enabling them to analyze petabytes of ‘omics data, which would usually take several generations of experimentation.

The “cognitive engine” uses machine learning “to grow smarter and make better decisions with every data set and experiment” and does not require coding experience or advanced computational training, according to Benson Hill.

“Benson Hill totally changes the game and allows both small and large companies to improve plant biology faster,” said Dan Watkins, partner at Mercury Fund, which invested in Benson Hill’s $7.3 million Series A round last year.

“Previously, only the largest companies or research institutions had the resources and expertise to do this and, even then, it could take years to get a research program to market. CropOS represents a uniquely powerful platform at the intersection of big data, machine learning, and plant biology. Perhaps most impressive is that CropOS has already demonstrated, in ongoing field trials, that it can drive very significant increases in yield for major food crops.”

We caught up with Matt Crisp, CEO of Benson Hill, to discuss the company’s journey to this point, and to hear more about his career, which spans roles at Intrexon, where he launched an agricultural biotechnology division for the synthetic biology company, and Third Security, a venture capital firm.

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