Overview of Traditional and Generative AI Applications at the USDA's National Agricultural Statistics Service

Music City Center 

The USDA's National Agricultural Statistics Service (NASS) has integrated traditional AI into a number of its production processes. Privacy Preserving Record Linkage (PPRL) using Natural Language Processing (NLP) provides the foundation for integrating survey and non-survey data. Response propensity models have been used to inform sampling and data collection. Traditional AI methods are utilized in the development of geospatial products. For example, the Cropland Data Layer (CDL) displays where each of 114 crops are grown across the contiguous U.S. each year, and it forms the foundation for identifying the impacts of natural disasters on agriculture. Other models based on high-order Markov Chains and neural networks are used to predict what crops will be planted for an upcoming growing season, and maps of uncertainty are based on normalized Shannon entropy. Some machine learning models provide insights for imputation. Other models provide the foundation for producing official statistics. NASS has hundreds of programs, many of which were written in code that is no longer supported, not recommended for use, or too expensive. Although the agency does not have the resources to pay someone to convert the code to a more modern language, generative AI is a feasible solution. In this presentation, progress that NASS has made in adopting traditional and generative AI methods and the future of generative AI within the agency will be discussed.