Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

  • 1023 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Haboudane, D., Tremblay, N., et al.: Estimation of plant chlorophyll using hyperspectral observations and radiative transfer models: spectral indices sensitivity and crop type effects. In: IGARSS. IEEE (2008). 978-1-4244-2808-03

    Google Scholar 

  2. Hatfield, J.L., Gitelson, A.A., Scherpers, J.S., et al.: Application of remote sensing for agronomic decisions. Agron. J. 100, S-117–S-131 (2006)

    Google Scholar 

  3. Holecz, F., Barbieri, M., Collivignarelli, F., Gatt, L.: An operational remote sensing based service for rice production estimation at a national scale. In: Proceeding of ESA Living Planet Symposium, pp. 1–11 (2013)

    Google Scholar 

  4. Lehmann, J.R.K., Oldeland, J., Romer, M.: Field spectroscopy in the VNIR-SWIR region to discriminate between Mediterranean native plants and exotic-invasive shrubs based on leaf tannin content. Remote Sens. 7, 1225–1241 (2015). https://doi.org/10.3390/rs70201225

    Article  Google Scholar 

  5. Ling, C., Liu, H., Ju, H., Zhang, H., You, J., Li, W.: A study on spectral signature analysis of wetland vegetation based on ground imaging spectrum data. J. Phys. 910, 012045 (2017)

    Google Scholar 

  6. Silleos, N., Misopolinos, N., Perkis, K.: Relationship between remote sensing spectral indices and crops discrimination. Geocarto Int. 7(2), 41–51 (1992)

    Article  Google Scholar 

  7. Verma, K.S., Saxena, R.K., Hajare, T.N., Ramesh-Kumar, S.C.: Gram yield estimation through SVI under soil and management conditions. Int. J. Remote Sens. 19, 2469–2476 (1998)

    Article  Google Scholar 

  8. Stanhill, G., Kafkafi, U., Fuchs, M., Kagan, Y.: The effect of fertiliser application on solar reflectance from a wheat crop. Israel J. Agric. 22, 109–118 (1972)

    Google Scholar 

  9. Sridhar, V.N., Dadhwal, V.K., Chaudhari, K.N., Sharma, R., Bairagi, G.D., Sharma, A.K.: Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh. Int. J. Remote Sens. 15, 1307–1316 (1994)

    Article  Google Scholar 

  10. Reynolds, C.A., Yitayew, M., Slack, D.C., Hutchinson, C.F., Huete, A., Petersen, M.S.: Estimating crop yields and production by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data. Int. J. Remote Sens. 21, 3487–3508 (2002)

    Article  Google Scholar 

  11. Dadhwall, V.K., Sridhar, V.N.: A non-linear regression for vegetation indexcrop yield relation incorporating acquisition date normalization. Int. J. Remote Sens. 18, 1403–1408 (1997)

    Article  Google Scholar 

  12. Maselli, F., Romanelli, S., Bottai, L., Andmaracchi, G.: NDVI data for yield forecasting in the Sahelian region. Int. J. Remote Sens. 21, 3509–3523 (2000)

    Article  Google Scholar 

  13. Surase, R.R., Varpe, A., Solankar, M., Gite, H., Kale, K.: Development of non-imaging spectral library via Field Spec4 spectroradiometer. Int. J. Res. Eng. Appl. Manage. (IJREAM). ISSN 2454-9150 Special Issue - NCCT – 2018

    Google Scholar 

  14. Magney, T.S., Griffin, K.L., Eitel, J., Vierling, L.A., et al.: Spectral determination of concentrations of functionality diverse pigments in increasing complex arctic tundra canopies. Oecologia 182(1), 85–97 (2016)

    Article  Google Scholar 

  15. Arun Prasad, K., Gnanappazham, L.: Species discrimination of mangroves using derivative spectral analysis. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. II-8, 45 (2014)

    Article  Google Scholar 

  16. Mazer, A.S., Lee, M., et al.: Image processing software for imaging spectrometry analysis. Remote Sens. Environ. 24(1), 201–210 (1988)

    Article  Google Scholar 

  17. Bannari, A., Morin, D., Bonn, F., Huete, A.R.: A review of vegetation indices. Remote Sens. Rev. 13, 95–120 (1995)

    Article  Google Scholar 

  18. Blackburn, G.A.: Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Int. J. Remote Sens. 19(4), 657–675 (1998)

    Article  MathSciNet  Google Scholar 

  19. Daughtry, C.S.T., et al.: Discriminating crop residues from soil by short-wave infrared reflectance. Agron. J. 93, 125–131 (2001)

    Article  Google Scholar 

  20. Cohen, W.B.: Response of vegetation indices to changes in three measures of leaf water stress. Photogramm. Eng. Remote Sens. 57(2), 195–202 (1991)

    MathSciNet  Google Scholar 

  21. Haboudane, D., Miller, J.R., Tremblay, N., et al.: Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81(2–3), 416–426 (2002)

    Article  Google Scholar 

  22. Kim, Y., Michael Glenn, D., Park, J., Lehman, B.L.: Hyperspectral image analysis for plant stress detection. An ASABE Meeting presentation, Paper Number-1009114 (2010)

    Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, MH, India

    Rupali R. Surase, Karbhari V. Kale, Mahesh M. Solankar, Amarsinh B. Varpe & Hanumant R. Gite

  2. Solapur University, Solapur, India

    Amol D. Vibhute

Authors
  1. Rupali R. Surase
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karbhari V. Kale
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahesh M. Solankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amarsinh B. Varpe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hanumant R. Gite
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amol D. Vibhute
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rupali R. Surase .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Surase, R.R., Kale, K.V., Solankar, M.M., Varpe, A.B., Gite, H.R., Vibhute, A.D. (2019). Crop Discrimination Based on Reflectance Spectroscopy Using Spectral Vegetation Indices (SVI). In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9187-3_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation