Daniela Jones
Department of Biological and Agricultural Engineering
Assistant Professor
JOINT APPOINTMENT WITH IDAHO NATIONAL LABORATORY
4168B Plant Sciences Building
Bio
Dr. Jones is a Research Assistant Professor in the Biological and Agricultural Engineering Department at North Carolina State University and holds a Joint-faculty appointment with Idaho National Laboratory. At NCSU, Dr. Jones is also the Data Science Academy Director of Agricultural Analytics; Director of the Agricultural Data Science Certificate; Office of Research and Innovation Faculty Fellow; Graduate Faculty of the Operations Research Program; Faculty Fellow of the Center of Geospatial Analytics; Faculty Affiliate of the Southeast Climate Adaptation Science Center; and Faculty Affiliate of the Agricultural Biotechnology In Our Evolving Food, Energy, and Water Systems NSF Research Traineeship Program.
She earned her PhD in Biological and Agricultural Engineering with a concentration on energy systems from Texas A&M University, where she was an Alfred P. Sloan Scholar and received a certificate in Business Management. She received her Masters and Bachelor of Science degrees in Industrial Engineering with an emphasis in operations research and a Minor in Mathematics from Mississippi State University. She interned at Idaho National Laboratory and collaborated with multidisciplinary teams at Oak Ridge National Laboratory through her work on biofuels and renewable energy. Before this role, she was a postdoctoral associate at Duke University, where she performed quantitative and qualitative research on student interventions and supported programming of educational, career development workshops and community development events for underrepresented undergraduate and graduate students in the biosciences.
Education
Ph.D. Biological and Agricultural Engineering Texas A&M University 2017
M.S. Industrial Engineering Mississippi State University 2012
B.S. Industrial Engineering Mississippi State University 2009
Publications
- Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence , COMPUTERS AND ELECTRONICS IN AGRICULTURE (2024)
- Characterizing value-added pellets obtained from blends of miscanthus, corn stover, and switchgrass , RENEWABLE ENERGY (2024)
- Decisiones Basadas en Datos para la Alimentación y la Energía , (2024)
- Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes , Smart Agricultural Technology (2024)
- Manure nutrient cycling in US animal agriculture basins-North Carolina case study , JOURNAL OF ENVIRONMENTAL QUALITY (2024)
- Nth-plant scenario for blended pellets of Miscanthus, Switchgrass, and Corn Stover using multi-modal transportation: Biorefineries and depots in the contiguous US , BIOMASS & BIOENERGY (2024)
- Predicting sweetpotato traits using machine learning: Impact of environmental and agronomic factors on shape and size , COMPUTERS AND ELECTRONICS IN AGRICULTURE (2024)
- Cultivating PhD Aspirations during College , CBE-LIFE SCIENCES EDUCATION (2022)
- Data-Driven Decisions for Food and Energy , (2022)
- Fostering Responsible Innovation through Stakeholder Engagement: Case Study of North Carolina Sweetpotato Stakeholders , Sustainability (2022)
Grants
A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain
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.
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.
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.