Michael Kudenov

Department of Electrical and Computer Engineering
Professor
College of Engineering
3307 Plant Sciences Building
mwkudeno@ncsu.eduBio
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
- Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks , PLANT METHODS (2025)
- A Comparison of Three Automated Root-Knot Nematode Egg Counting Approaches Using Machine Learning, Image Analysis, and a Hybrid Model , Plant Disease (2024)
- Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence , COMPUTERS AND ELECTRONICS IN AGRICULTURE (2024)
- Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes , SMART AGRICULTURAL TECHNOLOGY (2024)
- Exceptional Alignment in a Donor-Acceptor Conjugated Polymer via a Previously Unobserved Liquid Crystal Mesophase , ADVANCED FUNCTIONAL MATERIALS (2024)
- Flexible Self-Powered Organic Photodetector with High Detectivity for Continuous On-Plant Sensing , ADVANCED OPTICAL MATERIALS (2024)
- Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry , Plant Phenomics (2024)
- The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques , PLOS ONE (2024)
- Hybrid Mueller matrix spectral and polarimetric imaging for high throughput plant phenotyping , (2023)
- Hybrid spatial-temporal Mueller matrix imaging spectropolarimeter for high throughput plant phenotyping , APPLIED OPTICS (2023)