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.
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
- Flexible Self-Powered Organic Photodetector with High Detectivity for Continuous On-Plant Sensing , ADVANCED OPTICAL MATERIALS (2024)
- Hybrid spatial-temporal Mueller matrix imaging spectropolarimeter for high throughput plant phenotyping , APPLIED OPTICS (2023)
- Cephalopod-inspired snapshot multispectral sensor based on geometric phase lens and stacked organic photodetectors , OPTICAL ENGINEERING (2022)
- Drone-based polarization imaging for phenotyping peanut in response to leaf spot disease , POLARIZATION: MEASUREMENT, ANALYSIS, AND REMOTE SENSING XV (2022)
- Flexible sensor patch for continuous carbon dioxide monitoring , FRONTIERS IN CHEMISTRY (2022)
- Multistatic fiber-based system for measuring the Mueller matrix bidirectional reflectance distribution function , APPLIED OPTICS (2022)
- Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras , OPTICS EXPRESS (2022)
- StarNAV with a wide field-of-view optical sensor , ACTA ASTRONAUTICA (2022)
- Bio-inspired spectropolarimetric sensor based on tandem organic photodetectors and multi-twist liquid crystals , OPTICS EXPRESS (2021)
- Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery , Computers and Electronics in Agriculture (2021)
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
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 CarolinaÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s sweet potato growers.
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 projectâ€™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.
More than a third of crop yields are currently lost due to abiotic and biotic stressors such as drought, pests, and disease. These stressors are expected to worsen in a warmer, drier future, resulting in crop yields further declining ~25%; however, breeding is only expected to rescue 7-15% of that loss . The plant microbiome is a new avenue of plant management that may help fill this gap. All plants have fungi living inside their leaves (ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œfoliar fungal endophytesÃƒÂ¢Ã¢â€šÂ¬Ã‚Â). This is an ancient and intimate relationship in which the fungi affect plant physiology, biotic and abiotic stress tolerance, and productivity. For example, some foliar fungi prevent or delay onset of major yield-limiting diseases caused by pathogens such as Fusarium head blight . Foliar endophytes also reduce plant water loss by up to half and delay wilting by several weeks [3, 4]. Endophyte effects on plants occur via diverse genes and metabolites, including genes involved in stress responses and plant defense . Genes and metabolites also predict how interactions in fungal consortia affect host stress responses, which is important for developing field inoculations . Because newly emergent leaves lack fungi, endophytes are also an attractive target for manipulation (particularly compared to soils, where competition with the existing microbial community inhibits microbial additives). We propose to address the role of endophytic ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œmycobiomesÃƒÂ¢Ã¢â€šÂ¬Ã‚Â in stress tolerance of five North Carolina food, fiber, and fuel crops (corn, hemp, soybean, switchgrass, wheat), and to develop tools that can push this field beyond its current limits. Our major objectives (Fig. 1) are to: 1. Identify key microbiome scales to optimally manage endophytes 2. Determine fungal mechanisms via greenhouse tests, modeling, and genetic engineering 3. Build tools for field detection of endophytes 4. Understand the regulatory environment and engage diverse stakeholders Results of these objectives will allow us to make significant progress in both understanding the basic biology of plant-fungal interactions and managing those interactions in real-world settings. Our extension efforts will also bring these ideas to the broader community. Finally, we will also be well positioned to pursue several future research endeavors supported by federal granting agencies.