FACT: MACHINE-LEARNING-BASED IN-SEASON CROP MAPPING AND ASSOCIATED CLOUDBASED BIGDATA CYBERINFRASTRUCTURE TO SUPPORT USDA NASS DECISION MAKING

The goal of the project is to facilitate timely and informed agricultural decision making by developing the capability of generating and providing in-season CDL-like crop maps for CONUS through easy-to-use cyberinfrastructure. The specific objectives of this project is to 1) develop bigdata classification algorithms to automatically derive in-season CDL for CONUS; 2) enhance CropScape by implementing the algorithms as web services; and 3) migrate the enhanced CropScape to a cloud for better user support. In-season CDL means to have CDL-compatible product with reasonable accuracy at beginning of a growing season, continue to improve the product with season progress, and reach the accuracy similar to NASS CDL around early July.