DSFAS-AI: Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER)
The DECIDE-SMARTER project will provide democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain. Working with stakeholders in the sweetpotato value chain, the project will create a software asset to help growers make difficult decisions, design experiments to understand human cognitive abilities, identify potential sources of bias in the DI process, and develop a modeling, simulation, and visualization framework for implementing multiple DI models. Leveraging the ongoing research and data acquisition of the Sweet-APPS team at NC State, this project aims to reduce agricultural waste and maximize yield for North Carolina's sweet potato growers.
Sponsor
US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)
The grant—running from June 15, 2022 to June 14, 2025—is for a total of $649,722.
Principle Investigators
David L Roberts
Michael Kudenov
Cranos Williams
Daniela Sofia Jones
Sarah Kathleen Barnhill
More Details
The Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) project will lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain, filling a longstanding gap between technology and decision makers. Through a process of participatory design, the project team will work with stakeholders in the sweetpotato value chain to: 1) Create a software asset that helps growers with an otherwise difficult decision; 2) conduct experiments that inform the best software interfaces possible to support complex agricultural decision making (through characterizing, understanding, and leveraging human cognitive abilities; 3) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 4) develop a reference architecture and prototype implementation of a modeling, simulation, and visualization framework for implementing multiple DI models with agriculture stakeholders. The project will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweetpotato Analytics for Produce Provenance and Scanning (Sweet-APPS) team, a multi-disciplinary endeavor that aims to reduce agricultural waste and maximize yield for North Carolina’s sweet potato growers.