Discrimination Between Healthy and Diseased Cotton Plant by Using Hyperspectral Reflectance Data

  • Priyanka Uttamrao RandiveEmail author
  • Ratnadeep R. DeshmukhEmail author
  • Pooja V. JanseEmail author
  • Rohit S. GuptaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


Cotton is major cash crop in India. Whenever disease occurs on the plant it causes reduction in production and also it effects on economy. Traditional way of monitoring disease is very hectic and time consuming. Healthy and Diseased leaves of cotton plant are collected from Harsul Sawangi regions of Aurangabad region. In this study ASD FieldSpec4 Spectroradiometer device is used for collection of hyperspectral data of cotton plant. This paper aims to examine the effect of disease on cotton plant. Spectral data is compared statistically. Discrimination is done among the healthy and diseased leaves for different regions of electromagnetic radiation. Ranges of Region: Blue (400 nm–525 nm), Green (525 nm–605 nm), Yellow (605 nm–655 nm), Red (655 nm–750 nm), and NIR (750 nm–1800 nm). Found higher reflectance in healthy leaves of than the diseased leaves of cotton plant.


Cotton crop ASD FieldSpec4 Hyperspectral data Discrimination Remote sensing 



DST-FIST has supported this work with sanction number- SR/FST/ETI340/2013. Authors are thankful to DST-FIST and Department of Computer Science and Information Technology of Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India. For providing necessary infrastructure and support.


  1. 1.
    Prabhakar, M., et al.: Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton. Comput. Electron. Agriculture 97, 61–70 (2013)CrossRefGoogle Scholar
  2. 2.
    Randive, P.U., Deshmukh, R.R., Janse, P.V., Kayte, J.N.: Study of detecting plant diseases using non-destructive methods: a review. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 7(1), 66–71 (2018)Google Scholar
  3. 3.
    Slonecker, E.T.: Analysis of the effects of heavy metals on vegetation hyperspectral reflectance properties. In: Thenkabail, P.S., Layon, J.G., Huete, A. (eds.) Hyperspectral Remote Sensing of Vegetation, pp. 561–578 (2012)CrossRefGoogle Scholar
  4. 4.
    Atherton, D., Choudhary, R., Watson, D.: Advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants prior to visual disease symptomonology. Int. J. Agric. Environ. Res. 03(03) (2017)Google Scholar
  5. 5.
    Li, J., Li, C., Zhao, D., Gang, C.: Hyperspectral narrowbands and their indices on assessing nitrogen contents of cotton crop applications. In: Thenkabail, P.S., Layon, J.G., Huete, A. (eds.) Hyperspectral Remote Sensing of Vegetation, pp. 579–589 (2012)CrossRefGoogle Scholar
  6. 6.
    Gitelson, A.A.: Nondestructive estimation of foliar pigment (chlorophylls, carotenoids, and anthocyanins) contents: evaluating a semianalytical three-band model. In: Thenkabail, P.S., Layon, J.G., Huete, A. (eds.) Hyperspectral Remote Sensing of Vegetation, pp. 141–165 (2012)CrossRefGoogle Scholar
  7. 7.
    Curran, P.J., Dungan, J.L., Gholz, H.L.: Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiol. 7, 33–48 (1990)CrossRefGoogle Scholar
  8. 8.
    Filella, I., Serrano, L., Serra, J., Penuelas, J.: Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 35, 1400–1405 (1995)CrossRefGoogle Scholar
  9. 9.
    Blackburn, G.A.: Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ. 66, 273–285 (1998)CrossRefGoogle Scholar
  10. 10.
    Gloud, K., Kevin, D., Winefield, C. (eds.) Anthocyanins: Biosynthesis, Functions and Applications, p. 330. Springer, NewYork (2008). Scholar
  11. 11.
    Janse, P.V., Deshmukh, R.R.: Hyperspectral remote sensing for agriculture: a review. Int. J. Comput. Appl. (0975–8887) 172(7) (2017)Google Scholar
  12. 12.
    Jensen, J.R.: Remote Sensing of the Environment: An Erath Resource Perspective. Prentice-Hall, Upper Saddle River (2000)Google Scholar
  13. 13.
    Hunt, J., Ramond, E., Rock, B.N.: Detection in changes in leaf water content using near and mid-infrared reflectance. Remote Sens. Environ. 30, 45–54 (1989)Google Scholar
  14. 14.
    Ustin, S.L., Roberts, D.A., Green, R.O., Zomer, R.J., Garcia, M.: Remote sensing methods monitor natural resources. Photon. Spectra 33(N10), 108–113 (1999)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information TechnologyDr. Babasaheb Ambedkar Marathawada UniversityAurangabadIndia

Personalised recommendations