When farmers want crops that have a better shot at resisting disease or taking up nutrients, they use breeding techniques to develop new varieties. Crossing plants to create these varieties is an agricultural practice that dates back thousands of years.
Breeding has since become more sophisticated, incorporating sophisticated screening technologies to find desirable traits. But the expense of these technologies puts them beyond the reach of farmers and small companies. Benson Hill Biosystems, which splits its operations between St. Louis, MO, and Durham, NC, is trying to level the field for those who don’t have access to advanced breeding technology. The agtech startup’s software, called CropOS, predicts biological outcomes, which helps farmers, greenhouses, and researchers discover traits and make better breeding decisions, says CEO Matthew Crisp.
CropOS works by analyzing vast amounts of genomic and biological data from both public and private sources. The software can then pinpoint which plants will produce the desired traits and improved performance that ag companies and growers want. Everything CropOS learns from analyzing data becomes part of a pool of information that grows as the software’s users add their data to the system. Crisp calls it “a networked model.”
There’s precedent for applying big data analysis to life sciences discovery, notably in the search for new drugs. But this approach is also taking hold in agtech research. Indigo, a Boston-based startup whose microbial seed coating was developed to help cotton plants hold up better in drought conditions, uses analytics to discover and develop its products. After the company raised $100 million in a Series C round in July, CEO David Perry explained to Xconomy that Indigo’s software uses machine-learning techniques to predict which microbes will help a plant when confronted with a stressor, such as drought.
Syngenta (NYSE: [[ticker:SYT]]) applies analytical tools to soybeans to make the process of designing, developing, and testing new varieties more efficient. The company says these analytical tools ultimately lead to soy varieties with higher yields while also reducing the cost of such research. Both Indigo and Syngenta crunch big data to develop their own products. Davis, CA-based Arcadia Biosciences (NASDAQ: [[ticker:RKDA]]) uses its technology to develop traits, such as nitrogen use efficiency. Arcadia then licenses those traits to seed and agbio companies that bring those traits into crops such as wheat, soy, and rice. That’s similar to Benson Hill’s approach. But the business model of Crisp’s company takes a page from the tech sector.
Cloud computing made storage and analysis of vast amounts of data accessible and affordable to small companies, democratizing information technology. Benson Hill is trying to do the same thing for smaller players in the ag sector. Crisp calls the approach “cloud biology.” But rather than providing data storage, Benson Hill offers software tools that help companies analyze big data in plants.
CropOS is the cognitive engine that drives Benson Hill’s technology. On top of that platform, the company offers an application called Reveal, which is used to discover and design trait candidates. Another application, Breed, helps make faster breeding decisions. Benson Hill markets its technology to businesses and farmers looking for ways to boost their own plant research efforts. Customers pay a subscription fee to access the software, which allows them to upload their crop data into the system. Analyzing that data, the software helps customers identify which
