Using machine learning to improve crop performance
“We need to empower more innovators to solve the world’s agricultural challenges,” said Benson Hill CoFounder and CEO Matthew Crisp. “By partnering CropOS with the broader agriculture community, from farmer coops to research institutions to businesses of all sizes, we are opening up innovation opportunities that were formerly accessible only to those with the largest and most well advanced research programs. Given the universal importance of agriculture in our global society, it’s crucial that we harness the power of cloud biology, and I’m excited that we are at the forefront of empowering this movement.”
Cloud biology
Agricultural technology is being upended by the convergence of big data analytics, cloud computing, and biological expertise. This emerging discipline known as Cloud Biology, empowers researchers to solve formidable agricultural challenges such as increasing the metabolism of plants to improve crop yield. Formerly a slow and often costprohibitive process constrained by compute availability, barriers to information and length of growing season, enhancing the genetics of plants can be greatly accelerated through improved decision support for breeders and researchers.
“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 $8M Series A round. “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.”
CropOS
CropOS enables users to analyze petabytes of genomic data to pinpoint which seeds will produce desired traits, allowing researchers to bypass multiple generations of experimentation. Instead of being constrained by greenhouse space, field plots or growing seasons, users define the desired result and leverage the power of a cognitive engine to help determine the genetics most likely to produce it. Desirable biofuel feedstocks like a higher biomass sorghum plant, for example, or a superfood like a more nutritious blueberry, can be successfully identified and advanced to market in a fraction of the time.
The significantly shortened timetomarket for improved crops is driven by the combination of biological knowledge of plants combined with genomics information and big data analytics. CropOS uses machine learning to grow smarter and make better decisions with every data set and experiment, allowing for increasingly sophisticated research over time. CropOS does not require coding experience or advanced computational training, and can be used by any plant breeder or researcher wishing to employ an advanced decision support tool to develop improved plant varieties or traits.
For more information:
bensonhillbio.com