Spectral and Numerical Analysis of Hyper spectral Data using Vegetation Indices
Nikhil M. Sapate1, Ratnadeep R. Deshmukh2
1Nikhil M. Sapate : M. Tech (CSE, Second Year), Department of CS and IT, Dr. BAMU, Aurangabad
2Dr. R. R. Deshmukh : Professor, Former Head, Dept. CS and IT, Dr. BAMU, Aurangabad.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2156-2162 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8578088619/2019©BEIESP | DOI: 10.35940/ijeat.F8578.088619
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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: Indian economy is majorly influenced by Agriculture and its allied sectors. More than 50% of the population of India is dependent on agriculture and its allied sectors for their survival. According to the Report of Indian Council of Agricultural Research, 30-35% of the crop yield gets wasted due to disease. Using modern day remote sensing techniques plant health can be monitored and it can be specified whether a plant is diseased or healthy. Hyperspectral Remote Sensing is the technique by which fine and minute information of vegetation can be obtained with the help of narrow wavebands. Data of 80 leaf samples of Tomato crop collected in spectra form and text form using ASD FieldSpec4 spectroradiometer and ViewSpec PRO. This information of plant leaves was used to identify vegetation attributes and its status. Vegetation Indices are calculated using mathematical formulae published in the previous study. Random forest classification used to discriminate among Healthy and Diseased plants. Algorithm works with an accuracy of 93.75% with misclassification rate 0.0625. Along with Green wavelength range and Red edge of the spectrum, specific disrupted behavior was observed in Shortwave Infrared Region of the spectra. The research paper focuses on Spectral and Numerical study and analysis of Tomato Leaf disease with the help of ASD FieldSpec4.
Keywords: ASD FieldSpec4 Spectroradiometer Hyperspectral Remote Sensing, Vegetation Indices, VNIR-SWIR Spectroscopy.