Daniela Jones

A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain

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.

This project addresses the challenges of concentrated manure volumes in hot-spots across NC, limited agricultural land base to accept these nutrients, and the adverse environmental impacts of manure management. We are proposing a the development of a framework to plan sustainable recycling and export of manure nutrients to preserve and restore air, soil, and water resources across the state of North Carolina. This project will leverage datasets, models, and regulations governing manure across NC to develop an alternative supply-chain for manure management to attain beneficial outcomes to the environment, economy, and society. This goal will be accomplished through the following objectives: (1) developing spatially-explicit datasets to quantify and characterize manure associated with different swine and poultry farm types, (2) developing models for upgrading and treatment technologies applicable to these manures, (3) developing technical/economic models to model performance of each technology alone or coupled, and (4) establishing a logistics optimization framework that integrate spatially-explicit residue datasets with compatible upgrading and recycling technologies.

Joint appointment release for Dr. Daniela Jones from Idaho National Laboratory with North Carolina State University

At peanut buying stations across the U.S. South East, peanut grading is currently implemented using labor-intensive equipment. Many of the steps related to grading have been unchanged for decades. A critical reason for this involves political pressures against updating or expediting the grading process. However, like many other economic sectors, new labor-force pressures are requiring that more be done with fewer people. Given that (1) labor is more challenging to come by; and (2) political pressure exists to maintain the status quo, we propose to update key steps in the existing process to simplify and/or expedite data collection. This project???s goal is to develop automated imaging and weighing technologies that can serve as a bridge, toward more fully automated systems, by addressing key bottlenecks in the existing grading process. We will achieve this by the following objectives: (1) Automate the weighing and grading of peanuts either traveling down or entering the rollers during pod pre-sizing; and (2) Automate the detection of splits and, if possible, sound versus unsound splits, by adding vision systems to the existing sheller.