Open AccessDisentangling the Relationship in Health of Zea Mays Crop using Photochemical Reflectance Index and Nitrogen Reflectance Index
Author(s) -
Komal Patil,
Prof. K. V. K
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1720.029420
Subject(s) - hyperspectral imaging , reflectivity , nitrogen , mean squared error , spectral index , linear regression , photochemical reflectance index , zea mays , mathematics , environmental science , statistics , remote sensing , agronomy , spectral line , leaf area index , chemistry , geography , biology , normalized difference vegetation index , physics , optics , organic chemistry , astronomy
Determining the spatial variation of different plant factors throughout growing season will help to resolve stress factors within a field in a timely basis. Whereas the spectral characterizes help to estimate the proper photosynthesis process. This research shows that the nitrogen reflectance index (NRI) help to predict the nitrogen level of healthy and diseased plants and photochemical reflectance index (PRI) affects the leaf spectral absorption. These indices are calibrated under the hyperspectral pushbroom camera Resonon PIKA-L (400-1000nm) which is non-destructive and less time consuming, it is available in RUSA lab in Dr. Babasaheb Ambedkar Marathawada University, Aurangabad, Maharashtra. The spectral bands considered for the calculation of NRI are 700nm, 670nm, 570nm and for PRI spectral bands considered were 531nm, 570nm. Statistical values for PRI were calculated like R-Square (0.727), RMSE (0.267), P-value (2.787), standard error (2.979) and the statistical values for NRI were R-Square (4.223), RMSE (0.512), P-value (0.968), standard error(2.648).Linear regression was calculated for finding the relation between the data.