Disentangling the Relationship in Health of Zea Mays Crop using Photochemical Reflectance Index and Nitrogen Reflectance Index
Komal. D. Patil1, K. V. Kale2
1Komal D. Patil*, Pursued Bachelor of Engineering, Computer Engineering, Pune University. Maharashtra, India.
2Prof. K. V. K, Senior Member IEEE, Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2646-2649 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1720029420/2020©BEIESP | DOI: 10.35940/ijitee.D1720.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Disease Index, Hyperspectral Signature, Linear Regression, Maize, PIKA-L sensor
Scope of the Article: Healthcare Informatics