Dr. Rajasheker Pullanagari
Research Officer
Agriculture and Environment
Massey University
New Zealand
Biography
Dr. Rajasheker Pullanagari is currently working as a Research Officer in the Department of Agriculture and Environment, Massey univesity , Newzealand. His research interests includes activities are focused on characterising and quantifying vegetation biophysical and biochemical features using remote sensing (proximal, air-borne and space-borne) as well as geographic information system (GIS) techniques.. He is serving as an editorial member and reviewer of several international reputed journals. Dr. Rajasheker Pullanagari is the member of many international affiliations. He has successfully completed his Administrative responsibilities. He has authored of many research articles/books related to activities are focused on characterising and quantifying vegetation biophysical and biochemical features using remote sensing (proximal, air-borne and space-borne) as well as geographic information system (GIS) techniques..
Research Interest
His current research activities are focused on characterising and quantifying vegetation biophysical and biochemical features using remote sensing (proximal, air-borne and space-borne) as well as geographic information system (GIS) techniques. Precision agriculture technologies Application of hyperspectral imaging for food quality inspection
Publications
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Pullanagari, RR., Kereszturi, ., & Yule, . (2017). Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data. International Journal of Applied Earth Observation and Geoinformation. 58, 26-35 Retrieved from http://www.elsevier.com/locate/jag
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Li, M., Pullanagari, RR., Pranamornkith, T., Yule, IJ., & East, AR. (2017). Quantitative prediction of post storage ‘Hayward’ kiwifruit attributes using at harvest Vis-NIR spectroscopy. Journal of Food Engineering. 202, 46-55
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Pullanagari, R., Kereszturi, G., Yule, IJ., & Ghamisi, P. (2017). Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network. Journal of Applied Remote Sensing. 11(2)
