@article{scarboro_ruzsa_doherty_kudenov_2021, title={Quantification of gray mold infection in lettuce using a bispectral imaging system under laboratory conditions}, volume={5}, ISSN={["2475-4455"]}, url={https://doi.org/10.1002/pld3.317}, DOI={10.1002/pld3.317}, abstractNote={Abstract}, number={3}, journal={PLANT DIRECT}, publisher={Wiley}, author={Scarboro, Clifton G. and Ruzsa, Stephanie M. and Doherty, Colleen J. and Kudenov, Michael W.}, year={2021}, month={Mar} }
 @article{kudenov_scarboro_2018, title={Synthetic neural network calibration of a hyperspectral imaging camera}, volume={10656}, ISSN={["1996-756X"]}, DOI={10.1117/12.2305521}, abstractNote={The use of machine learning algorithms is used extensively when analyzing and labeling image data. However, these techniques also offer additional post-processing advantages when they are used to calibrate sensor data. In this paper, a new calibration strategy for a Snapshot Hyperspectral Imaging Fourier Transform (SHIFT) spectrometer is discussed. The method, which is based on use of artificial neural networks, offers greater accuracy in both space and frequency when compared to conventional post-processing techniques. A theoretical model for the calibration procedure is presented, the laboratory data collection protocol is provided, and validation is conducted by measuring NIST-traceable spectral reflectance standards.}, journal={IMAGE SENSING TECHNOLOGIES: MATERIALS, DEVICES, SYSTEMS, AND APPLICATIONS V}, author={Kudenov, Michael W. and Scarboro, Clifton G.}, year={2018} }