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Michael Kudenov

Department of Electrical and Computer Engineering (College of Engineering)

Professor

3307 Plant Sciences Building

Bio

Dr. Kudenov completed his BS degree in Electrical Engineering at the University of Alaska Fairbanks in Fairbanks, AK in 2005. Upon graduation, his personal interest in astronomy and photography lead him to obtain his PhD in Optical Sciences at The University of Arizona (UA) in Tucson, AZ in 2009. Following his PhD, he remained as an Assistant Research Professor at the UA until departing for North Carolina State University in 2012. Research performed at the UA included visible and infrared imaging polarimetry, spectroscopy, 3D profilometry, interferometry, active learning, and lens design.

His current research is focused on developing novel imaging systems, interferometers, detectors, and anisotropic materials related to polarization and spectral sensing, for wavelengths spanning ultraviolet through the thermal infrared. He is particularly interested in developing novel anisotropic materials and detector technologies that better enable snapshot systems, which are capable of maximizing the spatial, spectral, and/or polarimetric information contained within a single image. Applications include biomedical imaging, remote sensing, food safety, 3D Imaging, and atmospheric monitoring.

Dr. Kudenov has authored 13 journal articles, 15 conference proceedings, 2 patents (pending), 1 book contribution, and is in the process of writing a new book on instrumentation. He is currently interested in obtaining undergraduate and graduate student researchers.

Education

Ph.D. Optical Sciences University of Arizona, Tucson 2009

M.S. Optical Sciences University of Arizona, Tucson 2007

B.S. Electrical Engineering University of Alaska Fairbanks 2005

Publications

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Grants

Date: 01/15/21 - 1/14/26
Amount: $238,500.00
Funding Agencies: US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)

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

Date: 09/01/21 - 8/31/25
Amount: $5,011,710.00
Funding Agencies: US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)

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

Date: 06/15/22 - 6/14/25
Amount: $649,722.00
Funding Agencies: US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)

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.

Date: 01/15/21 - 1/14/24
Amount: $500,000.00
Funding Agencies: US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)

Current plant breeding programs generate a disproportional amount of data in relationship to their ability to analyze and implement strategies for selection. For this reason and the lack of expertise in the area, plant breeding programs need to bridge the computational and programming acuity of computer scientists to harness the power of the data collected. Furthermore, pairing objective data collection methods in a high-throughput phenotyping approach provides consistency and efficiency, drastically improving the response to selection. This proposal aims to streamline the selection of cultivated peanut breeding lines for early (Mycosphaerella arachidicola) and late (Nothopassalora personata) leaf spot resistance through genomic data development and genome-wide associations for validation and early generation, marker-assisted selection; the development of a high-throughput phenotyping platform for leaf spot differentiation and quantification; and the use of the generated phenotypic information in parallel with genomic prediction models in genetically stable family nurseries to improve on leaf spot resistance and other agronomically important traits to the crop.

Date: 08/01/22 - 7/31/23
Amount: $448,100.00
Funding Agencies: Probelte

This proposal develops an expandable infrastructure for the field testing and deployment of plant protection products. The project is focused on the management of botrytis in strawberries in the United States but with potential global application. The project includes: i) the identification of microclimate and phenological indicators for botrytis forecasting; ii) affordable, infield sensor arrays; iii) Deployment of improved Botrytis forecasting models’; and iv) Product testing in strawberry fields including integration of models, infield monitoring and product application. Initial product testing is planned in North Carolina, Florida and California.


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