What do you do when you have access to satellite imagery of the entire world and a mission to better understand the planet ? You have the information needed to answer many important questions, but need a path to unlock those solutions: machine learning acts as the key to accomplish your goal.
Founded in 2014, Descartes Lab had a simple vision of making, “it easy to build models on top of huge amounts of geospatial data” . The lab collects five terabytes of satellite data daily; that is 2.5 billion printed pages, stacking to over 100 miles high . Given the extensive nature of the data and the causal relationship the lab was seeking, machine learning was the ideal megatrend for Descartes Lab to build a business around .
Descartes Labs, in its four shorts years, through building this database, has partnered with clients to address some of the world’s most crucial issues. One of these problems is global food security.
Background on the Food Scarcity Problem
In the next 30 years the population of the world is set to increase by 40%, resulting in the need for 70% more food . Until recently, addressing the issue of food scarcity in the context of population growth seemed unobtainable. Satellite imagery has been widely recognized as the third agricultural revolution and, by analyzing with machine learning, the main weapon to combat the food scarcity issue. Through combining many geospatial data sources, Descartes is able to create a complete picture of the state of the world today and by using machine learning better able to detect causal patterns in that dataset. 
Evolution of farming through time:
Short Term (next 2 years)—Finding the Right Projects/Partners
Descartes has worked on several key projects that are specifically targeted at addressing the food scarcity issue:
- Production Forecast (Initial Success)
The lab, starting with corn, spent much time analyzing crop data through machine learning to put together an algorithm that predicted crop yields. With the algorithm, Descartes was able to predict production yields within two percent of the USDA numbers, but was able to do so five months before the USDA numbers were released in 2016. The data is historically collected through USDA surveys sent monthly to farmers, but by moving the satellite date and analyzing through machine learning, Descartes is able to produce weekly reports. 
- DARPA Grant (Current Project)
Defense Advanced Research Projects Agency, DARPA, has invested over $7M in Descartes to fund a project meant to predict food supply in Africa and the Middle East. Understanding that food scarcity leads to political unrest, the U.S. government has tasked Descartes with analyzing the regions wheat production to create models that can be proactive in predicting scarcity problems. 
- Developing more Sophisticated Predictions—Weather
Descartes realizes that a key next step is better predicting natural disasters. At the end of July, the lab released a wildfire tracker as the first of hopefully many tools to help predict the movement of natural disasters and alert those most at risk .
Although extremely successful in its ventures thus far, Descartes has several other avenues they could choose to pursue that could further improve their ability to address food scarcity:
- Increase partnerships with firms that could leverage current findings
Descartes could partner with firms like Indigo, “a tech startup in Boston, Massachusetts, [that]makes seed treatments that help plants grow”, and other agriculture businesses to utilize product-information synergies to bring the lab’s crop findings to the market .
- Build capital
When information is virtually unlimited like it is for Descartes, the limiting resource then becomes capital. In a world where many institutional investors use the USDA production data to make trading decisions, with not much effort, Descartes could leverage its own predictive models to make better informed investment decisions. If their models are correct, these decisions will offer greater returns and thus provide capital to fund the lab. This may relieve some of the pressures Descartes has to charge for its models, assuming these models are outside the ones fueling the investments.
There are still questions that remain about the potential negative consequences of Descartes’ predictive models from such a powerful, global dataset. Questions to think about:
- Knowledge is power—balancing profits vs. safety
- How do you make sure Descartes machine learning findings in food scarcity issues are not used as tools to hurt societies?
- Market concentration
- There is now a large competitive advantage for companies who have access to these findings: how do you make sure the data is not used to eliminate competition and create market inefficiencies?
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