AI Case Study
The US Department of Natural Resources used deep learning vision analysis of satellite imagery to predict the size of soya and corn crops well before harvesting
Satellite imagery analysis has traditionally not being able to differentiate crops such as corn and soya. This has meant that it has taken up to six months after harvest to determine the yield of crops. Deep neural network machine vision trained on satellite imagery was able to detect the crop type based on looking at short-wave infrared data that reflected leaf water signatures. This meant that crops could for the first time be predicted before harvest.
"...corn and soybean have predictably different leaf water status by July most years. The team used short-wave infrared (SWIR) data and other spectral data from three Landsat satellites over a 15-year period, and consistently picked up this leaf water status signal.
The advancement, published in Remote Sensing of Environment, is a breakthrough because, previously, national corn and soybean acreages were only made available to the public four to six months after harvest by the USDA. The lag meant policy decisions were based on stale data. But the new technique can distinguish the two major crops with 95 percent accuracy by the end of July for each field – just two or three months after planting and well before harvest.
Budgeting And Forecasting
"Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes. However, there is no publically available spatially explicit in-season crop-type classification information for the U.S. Corn Belt (a landscape predominated by corn and soybean). Instead, researchers and decision-makers have to wait until four to six months after harvest to have such information from the previous year."
"In comparison with USDA's Crop Data Layer (CDL), this study found a relatively high overall accuracy (i.e. the number of the corrected classified fields divided by the number of the total fields) of 96% for classifying corn and soybean across all CLU fields in the Champaign County from 2000 to 2015. Furthermore, our approach achieved 95% Overall Accuracy by late July of the concurrent year for classifying corn and soybean."
Deep neural networks
"The team focused their analysis within Champaign County, Illinois, as a proof-of-concept. Even though it was a relatively small area, analyzing 15 years of satellite data at a 30-meter resolution still required a supercomputer to process tens of terabytes of data."