Dr. Keshav D Singh is a postdoctoral fellow in the College of Agricultural and Environmental Sciences, University of California, Davis CA. He has an expertise in Hyperspectral Imaging (HSI) system. He is working on applied use of remote sensing technologies in studies of host selection by selected insects and abilities to assess crop health via reflectance profiling (detection of crop responses to biotic and abiotic stressors). To assist with novel insight into improved calibration and processing of airborne remote sensing data to enhance classification accuracy and to promote the use of emerging UAS (unmanned aerial systems) technology in precision agricultural applications. Prior to joining UC Davis, he was an Assistant Professor at JECRC University, Jaipur, India where he taught "Engineering Physics" at Graduate level (B.Tech.), and "Remote Sensing, Astrophysics, GTR and Cosmology" at postgraduate level (M.Sc.). Prior to this, he also worked on “Hyperspectral Remote Sensing” as a Ph.D. graduate student at the Indian Institute of Technology Bombay, Mumbai. He did his Master of Technology (MS) in Engineering Physics and Bachelors in Physics (Hons.) with specialization in Astrophysics.
Hyperspectral imaging (HSI), an emerging technology developed in recent years, integrates conventional imaging and spectroscopy knowledge to attain both spectral and spatial information from an object. Imaging spectroscopy provide detailed signatures (such as reflectance) of the biological samples due to interaction between the electromagnetic radiation and contact material. It is a powerful tool in studies of host selection by selected insects and abilities to assess crop health via reflectance profiling (detection of crop responses to biotic stressors for precision agriculture). Abiotic stresses are drought (water deficit), excessive watering (waterlogging/flooding), extreme temperatures (cold, frost and heat), salinity (sodicity) and mineral (metal and metalloid) toxicity negatively impacts growth, development, yield and seed quality of crop and other plants. For this study, the hyperspectral data of various agricultural plants are acquired using an airborne “true push-broom” hyperspectral camera [OCI Imager (OCI-UAV-D1000), BaySpec Inc.; 116 bands from 450-970nm] mounted on a drone (S1000 Premium Octocopter). The acquired images are generally affected by ground reflectance and atmospheric conditions, so the bidirectional reflectance were corrected using Radiative Transfer Equation (RTE) based Hapke’s model, addressing non-linear factors arises due to multiple scattering. The hypercube data were elaborated and analyzed by an algorithm coded under MATLAB environment. The final classified images show that it is possible to pinpoint the areas covered by stress plants prior to pesticide spray over whole agriculture field. It reduces the time, cost of spray and poisonousness in our biosphere.