MFDS-m Red Edge Position Detection Algorithm for Discrimination Between Healthy and Unhealthy Vegetable Plants

  • Anjana GhuleEmail author
  • R. R. Deshmukh
  • Chitra Gaikwad
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


Spectral Reflectance of crop shows very distinguished sensitivity in spectral regions according to biophysical and biochemical parameters. Red Edge Position is the inflection point in the red edge region of electromagnetic spectrum which is between 680–780 nm. This is sensitive indicator of crop health. Red Edge Position is used to discriminate between healthy and unhealthy plants. Analytical Spectral Devices (ASD) Fieldspec spectroradiometer instrument having spectral range from 350 nm to 2500 nm, was used to collect lab spectra of vegetable plants. An algorithm is proposed based on a Maximum First Derivative Spread – mean and its reflectance magnitude in Red Edge Region. Maximum First Derivative Spread-mean (MFDS-m) algorithm is proposed to detect Red Edge Position which will be further used to discriminate healthy and unhealthy plants. Results are compared with Four Point Linear Interpolation, Extrapolation and Maximum First Derivative Techniques.


MFDS-m Spectral Reflectance Hyperspectral remote Sensing Red Edge Position 


  1. 1.
    Sahoo, R.N., Ray, S.S., Manjunath, K.R.: Hyperspectral remote sensing of agriculture special Section. Hyperspectral Remote Sens. Curr. Sci. 108(5), 10 (2015)Google Scholar
  2. 2.
    Horler, D.N.H., Dockray, M., Barber, J.: The red-edge of plant leaf reflectance. Int. J. Remote Sens. 4, 273–288 (1983)CrossRefGoogle Scholar
  3. 3.
    Dawson, T.P., Curran, P.J.: A new technique for interpolating red edge position. Int. J. Remote Sens. 19(11), 2133–2139 (1998)CrossRefGoogle Scholar
  4. 4.
    Lamb, D.W., Steyn-Ross, M., Schaare, P., Hanna, M.M., Silvester, W., Steyn-Ross, A.: Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: theoretical modelling and experimental observations. Int. J. Remote Sens. 23(18), 3619–3648 (2002)CrossRefGoogle Scholar
  5. 5.
    Guyot, G., Baret, F.: Utilisation de la haute résolution spectrale pour suivre l’état des couverts végétaux. In: Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, ESA SP-287, Assois, France, pp. 279−286 (1988)Google Scholar
  6. 6.
    Cho, M.A., Skidmore, A.K.: A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sens. Environ. 101, 181–193 (2006)CrossRefGoogle Scholar
  7. 7.
    Das, P.K., Choudhary, K.K., Laxman, B., Kameswara Rao, S.V.C., Seshasai, M.V.R.: A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data. Int. J. Remote Sens. 35(4), 1432–1449 (2014)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.IT DepartmentGECAurangabadIndia
  2. 2.CS and IT DepartmentDr.B.A.M. UniversityAurangabadIndia

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