AI Case Study
TellusLabs aims at providing accurate prediction of natural resources and agricultural yields with machine learning
TellusLabs leverages machine learning to analyse satellite imagery from NASA in combination with weather data from the National Oceanic and Atmospheric Administration and seasonal, crop-growing information from the U.S. Department of Agriculture. The analytics enable the company to generate predictions on natural resources and agricultural yields. In an internal test, the company demonstrated that it can project the end-of-year yield for U.S. corn more accurately than the government 69% of the time, using publicly available, historical USDA corn yield data.
"The company’s first foray into the market is Kernel, an agricultural commodities forecast modeling tool that recently entered a publicly accessible open-beta phase. The free, beta version of Kernel has limited features, but the full-fledged product is an interactive, online dashboard that shows a map of the main corn-growing regions in the U.S.—across 18 states—and key financial indicators, such as predicted yield, harvested area, and total production. Users can view data at a state, agricultural district, or county level and look at historical yield data sourced from the USDA. The dashboard also features an indicator arrow—analogous to a stock-market ticker—that denotes the average change in corn yield estimates, week over week. TellusLabs will update the forecasts daily."
Budgeting And Forecasting
"'There’s a broad base of people who have to make tough decisions around natural resources, and we want to give them quality data, quickly,' says TellusLabs CEO and cofounder David Potere."
"The company says a recent, internal test showed it is able to project the end-of-year yield for U.S. corn more accurately than the government. In the test, TellusLabs ran publicly available, historical USDA corn yield data from 2004 to 2014 through its algorithms and made predictions about year-end numbers. In that 10-year period, the startup’s estimates beat the government’s 69 percent of the time during August and September, which are the key trading months for corn."
machine learning algorithms
"Satellite imagery from NASA, weather data from the National Oceanic and Atmospheric Administration and seasonal, crop-growing information from the U.S. Department of Agriculture"