@article{henry_veazie_furman_vann_whipker_2023, title={Spectral Discrimination of Macronutrient Deficiencies in Greenhouse Grown Flue-Cured Tobacco}, volume={12}, ISSN={["2223-7747"]}, DOI={10.3390/plants12020280}, abstractNote={Remote sensing of nutrient disorders has become more common in recent years. Most research has considered one or two nutrient disorders and few studies have sought to distinguish among multiple macronutrient deficiencies. This study was conducted to provide a baseline spectral characterization of macronutrient deficiencies in flue-cured tobacco (Nicotiana tabacum L.). Reflectance measurements were obtained from greenhouse-grown nutrient-deficient plants at several stages of development. Feature selection methods including information entropy and first and second derivatives were used to identify wavelengths useful for discriminating among these deficiencies. Detected variability was primarily within wavelengths in the visible spectrum, while near-infrared and shortwave-infrared radiation contributed little to the observed variability. Principal component analysis was used to reduce data dimensionality and the selected components were used to develop linear discriminant analysis models to classify the symptoms. Classification models for young, intermediate, and mature plants had overall accuracies of 92%, 82%, and 75%, respectively, when using 10 principal components. Nitrogen, sulfur, and magnesium deficiencies exhibited greater classification accuracies, while phosphorus and potassium deficiencies demonstrated poor or inconsistent results. This study demonstrates that spectral analysis of flue-cured tobacco is a promising methodology to improve current scouting methods.}, number={2}, journal={PLANTS-BASEL}, author={Henry, Josh and Veazie, Patrick and Furman, Marschall and Vann, Matthew and Whipker, Brian}, year={2023}, month={Jan} }
 @article{jhuang_fuentes_jones_esteves_fancher_furman_reich_2019, title={Spatial Signal Detection Using Continuous Shrinkage Priors}, volume={61}, ISSN={["1537-2723"]}, url={http://dx.doi.org/10.1080/00401706.2018.1546622}, DOI={10.1080/00401706.2018.1546622}, abstractNote={Abstract Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. We apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field.}, number={4}, journal={TECHNOMETRICS}, publisher={Informa UK Limited}, author={Jhuang, An-Ting and Fuentes, Montserrat and Jones, Jacob L. and Esteves, Giovanni and Fancher, Chris M. and Furman, Marschall and Reich, Brian J.}, year={2019}, month={Oct}, pages={494–506} }