Michael Kudenov

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

We propose to deploy genomic and phenomic tools as an integrated approach for the development of superior sweetpotato varieties with robust resistance to M. enterolobii and M. incognita, and high storage root yield, shape and quality attributes that command a high market value. Beyond identifying the genetic components underpinning these traits, a breeding approach that accounts for the complex genetics of polyploidy (e.g. allele dose-dependent phenotypes) will be designed for combining multiple desirable traits in a single genetic background (i.e. multi-trait selection). This is particularly important in sweetpotato where a single important trait can break an otherwise remarkable variety. Resistance to GRKN and SRKNwill be studied within the context of a holistic nematode management strategy that maximizes economic and farm sustainability

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.

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.

Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmers������������������ and growers������������������ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This project������������������s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSU������������������s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.