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Vegetation Stress Detection with Hyperspectral Remote Sensing for a Winning Agribusiness


Affiliations
1 ESRI India, Mathura Road, New Delhi., India
2 Indian Statistical Institute, Agricultural and Ecological Research Unit (AERU), Kolkata., India
3 Remote Sensing Department, Birla Institute of Technology, Mesra, Ranchi., India
4 ESRI India, MCIA, Mathura Road, New Delhi., India
     

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The subject agribusiness has drawn an enormous attention by organized sector in national and multinational level with the recent move of the Government of India to allow Foreign Direct Investment (FDI) in the retailing sector. Technology driven efforts are very much important in this changed scenario to increase market efficiency reducing inventories, waste, and costs. Earth Observation Satellite (EOS) imagery driven Remote Sensing (RS) and Geographical Information System (GIS) technology can be utilized as a high-end Spatial Decision Support System (SDSS) to extract the different aspects of agriculture like land-use land-cover (LULC) condition, soil properties like moisture estimation, moisture conservation, crop identification, identification of suitable farming site for suitable crop, acreage estimation, crop monitoring, damage monitoring, and complete supply chain monitoring includes crop vehicle tracking integrating Global Positioning System (GPS). Increased availability of narrow band hyperspectral imagery from Hyperion sensor has prompted to explore hyeprspectral imagery to estimate the vegetation biophysical parameters and leaf biochemical used to detect nutritional and water stress condition. This paper summarizes the use of hyperspectral remote sensing for vegetation monitoring through biochemical and biophysical parameter estimation, discussing the potential for detecting water stress. Central to this objective is our primary research question: Can remote sensing play a key role to monitor agri-crop health to enhance the agribusiness efficiency?.

Keywords

Vegetation Stress, Hyperspectral Remote Sensing, Vegetation Index, Agricultural Monitoring, Agribusiness
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  • Vegetation Stress Detection with Hyperspectral Remote Sensing for a Winning Agribusiness

Abstract Views: 656  |  PDF Views: 2

Authors

Partha Pratim Ghosh
ESRI India, Mathura Road, New Delhi., India
Pabitra Banik
Indian Statistical Institute, Agricultural and Ecological Research Unit (AERU), Kolkata., India
Nilanchal Patel
Remote Sensing Department, Birla Institute of Technology, Mesra, Ranchi., India
Deb Jyoti Pal
ESRI India, MCIA, Mathura Road, New Delhi., India

Abstract


The subject agribusiness has drawn an enormous attention by organized sector in national and multinational level with the recent move of the Government of India to allow Foreign Direct Investment (FDI) in the retailing sector. Technology driven efforts are very much important in this changed scenario to increase market efficiency reducing inventories, waste, and costs. Earth Observation Satellite (EOS) imagery driven Remote Sensing (RS) and Geographical Information System (GIS) technology can be utilized as a high-end Spatial Decision Support System (SDSS) to extract the different aspects of agriculture like land-use land-cover (LULC) condition, soil properties like moisture estimation, moisture conservation, crop identification, identification of suitable farming site for suitable crop, acreage estimation, crop monitoring, damage monitoring, and complete supply chain monitoring includes crop vehicle tracking integrating Global Positioning System (GPS). Increased availability of narrow band hyperspectral imagery from Hyperion sensor has prompted to explore hyeprspectral imagery to estimate the vegetation biophysical parameters and leaf biochemical used to detect nutritional and water stress condition. This paper summarizes the use of hyperspectral remote sensing for vegetation monitoring through biochemical and biophysical parameter estimation, discussing the potential for detecting water stress. Central to this objective is our primary research question: Can remote sensing play a key role to monitor agri-crop health to enhance the agribusiness efficiency?.

Keywords


Vegetation Stress, Hyperspectral Remote Sensing, Vegetation Index, Agricultural Monitoring, Agribusiness

References