A research team led by Zhou Zhang, assistant professor of biological systems engineering, recently received funding for a project titled “DSFAS-AI: Developing an integrated deep learning model framework for county-level crop yield prediction in support of USDA NASS operation” through NIFA AFRI’s Data Science for Food and Agricultural Systems program. It was among 13 projects selected to share $7 million in funding.

Project description (from CRIS database): Accurate and timely estimation of regional crop yield is of great importance for food security monitoring, market planning, and farm resource management. The National Agricultural Statistics Service (NASS) publishes major crop yield predictions monthly at the state-level before harvest through NASS Crop Production Report. However, there are no monthly or weekly county-level crop yield estimates available from NASS report, though they are in heavy demand and critical to many stakeholders in the government, the academic community, and the private sector for their business decisions. Moreover, NASS’s crop yield estimation relies on in-season surveys which are costly, time-consuming, and labor-intensive. To fill this gap, this project aims to produce weekly in-season crop yield predictions over the continental United States (CONUS) at the county-level, by utilizing publicly available satellite remote sensing datasets and state-of-the-art deep learning (DL) technologies. Specifically, we will (1) Develop a Bayesian deep learning model to provide weekly crop yield predictions and uncertainties at the county-level in the CONUS; (2) Integrate the deep learning-based yield prediction model with unsupervised domain adaptation to improve the model performance in counties with no or limited reported yield data; and (3) Create a web-based cyber-platform to disseminate the integrated modeling toolkit and yield prediction results for free public use. This DL model predicated county-level yield will provide complementary information to the in-season NASS Crop Production Report, and enhance agricultural monitoring.