Agricultural Stress Monitoring using Remote Sensing Data
Kavita V. Bhosle1, Vijaya Musande2
1Kavita Bhosle*, Department of CSE, Maharashtra Institute of Technology, Aurangabad, Maharashtra, India.
2Dr. Vijaya Musande, Department of CSE, MGM’s Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 319-327 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4688029320/2020©BEIESP | DOI: 10.35940/ijeat.C4688.029320
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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: : This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.
Keywords: Atmospheric correction, Hyperspectral remote sensing data, Field Stress.