Crop Discrimination Based on Reflectance Spectroscopy Using Spectral Vegetation Indices (SVI)

  • Rupali R. SuraseEmail author
  • Karbhari V. Kale
  • Mahesh M. Solankar
  • Amarsinh B. Varpe
  • Hanumant R. Gite
  • Amol D. Vibhute
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


This paper represents three main objectives of research, including (1) development of crop spectral library for diverse crops, (2) combination of two varying spectral responses for crop benchmarking, (3) interpretation of spectral features using Spectral Vegetation Indices (SVI). Hyperspectral sensors were used for spectral development including Maize, Cotton, Sorghum, Bajara, Wheat and Sugarcane crops with Analytical Spectral Device (ASD) Spectroradiometer and Earth Observing (EO)-1 Hyperion dataset positioned at Aurangabad region by Latitude 19.897827 and Longitude 75.308666. In precision agriculture, the Spectral Vegetation Indices (SVI) delivers valuable information for crop discrimination and growth monitoring; the present research elaborates about five SVI. The spectral responses were collected at the ripening stage of crops at standard darkroom environment in the laboratory. It was found that there was a progressive correlation 0.92 with squared residual value 4.69 amongst ASD and EO-1 Hyperion. The significant spectral features were recognized inAnthrocyanin Reflectance Index 1 (ARI1) with R550, R700, for Moisture Stress Index (MSI) R1599, R819 wavelength respectively. The experimental analysis was performed using ENVI and python open source software and it was concluded that crops types were successfully discriminated based on spectral parameters with different band combinations.


Hyperspectral remote sensing Precision agriculture Spectral signature Spectral vegetation indices Regression model 



Authors would like to acknowledge for providing partial technical support under UGC SAP (II) DRS Phase-II, DST-FIST and NISA to Department of Computer Science & IT, Dr Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India and also thanks for financial assistance under UGC-BSR research fellowship for this research work.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rupali R. Surase
    • 1
    Email author
  • Karbhari V. Kale
    • 1
  • Mahesh M. Solankar
    • 1
  • Amarsinh B. Varpe
    • 1
  • Hanumant R. Gite
    • 1
  • Amol D. Vibhute
    • 2
  1. 1.Department of Computer Science and Information TechnologyDr. Babasaheb Ambedkar Marathwada UniversityAurangabadIndia
  2. 2.Solapur UniversitySolapurIndia

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