Improving Crop Productivity and Value Through Heterogeneous Data Integration, Analytics, and Decision Support Platforms

This project aims to develop a multi-modal sensing platform to integrate and analyze multi-dimensional data from the agricultural supply chain. Utilizing a trust-based, data management, integration and analytics framework, the platform will be able to characterize and quantify sources of variability across the supply chain, from cultural practices to storage and handling. With the goal of optimizing management practices, the platform will be piloted at NC State University's Horticultural Crops Research Station and a major commercial farm and distribution facility. Findings will be shared with producers and regulators through NC Cooperative Extension and be applicable to other horticultural crops produced in North Carolina and beyond.

Sponsor

Principle Investigators

Cranos Williams
Michael Kudenov
Natalie Genevieve Nelson
Daniela Sofia Jones
Anders Schmidt Huseth

More Details

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.