KEYWORDS: Biosensors, Sensors, System on a chip, Digital imaging, Reflectivity, Remote sensing, Imaging systems, Baryon acoustic oscillations, Scene simulation, Spectral resolution
Automated detection of chemical threats is essential for an early warning of a potential attack. Harnessing
plants as bio-sensors allows for distributed sensing without a power supply. Monitoring the bio-sensors requires
a specifically tailored hyperspectral system. Tobacco plants have been genetically engineered to de-green when
a material of interest (e.g. zinc, TNT) is introduced to their immediate vicinity. The reflectance spectra of the
bio-sensors must be accurately characterized during the de-greening process for them to play a role in an effective
warning system. Hyperspectral data have been collected under laboratory conditions to determine the key regions
in the reflectance spectra associated with the degreening phenomenon. Bio-sensor plants and control (nongenetically
engineered) plants were exposed to TNT over the course of two days and their spectra were measured
every six hours. Rochester Institute of Technologys Digital Imaging and Remote Sensing Image Generation
Model (DIRSIG) was used to simulate detection of de-greened plants in the field. The simulated scene contains a
brick school building, sidewalks, trees and the bio-sensors placed at the entrances to the buildings. Trade studies
of the bio-sensor monitoring system were also conducted using DIRSIG simulations. System performance was
studied as a function of field of view, pixel size, illumination conditions, radiometric noise, spectral waveband
dependence and spectral resolution. Preliminary results show that the most significant change in reflectance
during the degreening period occurs in the near infrared region.
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