Recipient Organization
NORTH CAROLINA STATE UNIV
(N/A)
RALEIGH,NC 27695
Performing Department
Computer Sciences
Non Technical Summary
Artificial Intelligence (AI) advances have revolutionized industries (e.g., manufacturing, medicine); yet, the power of AI remains elusive for most agriculture stakeholders. This is exacerbated by technologists, which tend to focus on technology and solutions, as opposed to stakeholders and problems. This project will advance Decision Intelligence (DI) as a stakeholder- and problem-first approach. DI, which has been used effectively in industry for decades but not formalized academically, will enable new ways to link AI technology solutions with stakeholder expertise and domain-specific models, visualization tools, and built-in methods of identifying biases. Through participatory design with stakeholders, and using the sweetpotato supply chain as a use-case, the project's objectives are to: 1) create software for visualizing data-driven decisions; 2) conduct experiments to improve user decision making; 3) identify sources of bias that would reduce technology access and adoption; and 4) develop an open-source software reference architecture for DI tools. This project will provide a formalized methodology for users to identify and implement technology solutions in evolutionary and tractable ways, and addresses the DSFAS-AI topics of facilitating real-time decision making, incorporating new methods to reduce bias in machine learning methods, and developing open-source platforms for improved adoption of AI tools. With increasing innovations in on-farm AI capabilities, a rising tide of data is primed to overwhelm stakeholders without proper tools. Stemming the tide will require innovation in sensing, AI, software, cognition, and agriculture--all areas the project team comprises expertise--indicating the project is timely and the team is well-suited to meet producers' needs.
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
0%
Developmental
50%
Goals / Objectives
The DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) in Agriculture project is designed to lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agri- culture value chain. Nearly 10 decades of developments in Artificial Intelligence (AI) have yielded incredible breakthroughs that have revolutionized industries from manufacturing, to medicine, to automotive, and beyond; however, the power of those advancements remains elusive for most, re- quiring highly-specialized technical skills, access to data and computing resources, and software that is out of reach for most stakeholders. Further, much of the progress in AI has followed a similar path: the focus has been on developing highly-specialized solutions to relatively simple decisions in isolation. Real-world decision making is rarely so simple. Especially in agriculture value chains, multiple stakeholders with individual goals and circumstances are making many de- cisions with implications in both space and time (e.g., which fields to plant with which varieties of seed and when) and with heavy influences from external factors (e.g., market pricing, climate change, weather events).DI is a practice of understanding, modeling, simulating, and improving decision making in complex, multistakeholder environments that has decades of demonstrated success in a variety of industries from telecommunications, to finance, to public health; however, like AI, DI remains elu- sive to most stakeholders as the expertise required to implement DI methods is highly-specialized and, as a general rule, only available to customers or businesses with deep pockets that can con- tract for customized solutions. The DECIDE-SMARTER in Agriculture project will change this landscape by providing the software support that will make DI available to a broad range of stake- holders without the need for technical expertise, expensive and complicated computing infrastruc- ture, or even extensive sources of data. Through a process of participatory design, the DECIDE- SMARTER project team will work with stakeholders in the sweetpotato value chain to: 1) char- acterize, understand, and leverage human cognitive abilities in the design of software interfaces to visualize decisions; 2) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 3) develop a reference architecture and pro- totype implementation of a modeling, simulation, and visualization framework for implementing DI models with agriculture stakeholders. To facilitate these contributions, the DECIDE-SMARTER team will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweet- potato 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.
Project Methods
There are three philosophies guiding the approach taken in the DECIDE-SMARTER project:Stakeholder interaction is of paramount importance through all phases of research and software development to ensure their needs are addressed.Iteration is critical to ensure that the ultimate products delivered truly meet the needs of stakeholders across a wide variety of communities.Evaluation of all project activities at regular points in the project lifecycle will provide valuable insight into progress and both long- and short-term future directions.The first six months of the project will emphasize planning and conducting stakeholder interaction workshops to obtain a preliminary set of requirements for the DECIDE-SMARTER software and initial Causal Decision Models. In parallel with workshop planning, developing a "discovery prototype" of the DECIDE-SMARTER software built from "off-the-shelf" components selected to support use cases identified through stakeholder discussions will commence. We will conduct the workshops with existing stakeholder groups from NC Sweetpotato Field Days, organized by NCSU extension faculty affiliated with the Plant Sciences Institute.The DECIDE-SMARTER project will be organized into six cycles of innovation, each with a regular schedule of planning, development, and evaluation that will be punctuated with stakeholder interactions at strategic points in the process. This iterative approach to managing the project will follow the well-established Agile methodology which is considered to be best practice in software development. In addition to the agile development of the DECIDE-SMARTER software architecture and prototypes, we will also use Agile methodology for overall project management.Building on the discoveries from each iteration through the continuous improvement cycle, we will introspect, diagnose, and plan improvements to enhance all aspects of the project's activities, from the composition of stakeholder groups and types of interactions to fundamental research goals, processes, and outcomes. Using a combination of internal evaluations by project personnel (i.e., surveys and stakeholder advisors), we will obtain diverse feedback. Where appropriate, quantitative measures of performance will be used to adjust processes, goals, and methods. These evaluations will also be used as part of regular internal progress reviews and as part of the process for prioritizing and sunsetting activities. The six cycles of innovation in this project will be organized into planned "sprints" as follows: 1) post hoc analysis of prior cycle (0.75 months), 2) planning for new goals and deliverables (0.75 months); 3) iterative development, integration, internal testing, and internal evaluation (four 1-month sprints); and 4) deployment and external evaluation (0.5 months). The six-month iterations coincide with stakeholder engagement activities, including sweetpotato field days, which maximizes opportunities for the DECIDE-SMARTER team to receive stakeholder feedback.The project objectives include 1) prototyping visualizations, 2) developing a reference software architecture, 3) integrating research advances and existing AI assets into an implementation of the reference architecture, and 4) developing a framework for identifying biases in DI processes and tools. Evaluating these objectives will occur primarily through quantitative and qualitative user studies, focus groups, and stakeholder interviews.