Correlation of light wavelengths on spectral cameras and vegetation indexes in barley crop scouting

  • 1 University of Belgrade, Faculty of Mechanical Engineeing, Department of Agricultur Machinery, Serbia
  • 2 University of Belgrade, Faculty of Agriculture, Institute of Agricultural Engineeing, Nemanjina, Serbia


This paper presents a case study of a barley field experiment that was periodically scouted using a drone spectral camera. The camera has 4 bands so barley was scouted using 4 wavelengths of light – Green, Red, Red Edge and Nir Infra Red (NIR). Based on these wavelengths it is possible to calculate different vegetation indexes known in science and practice. In this paper, 15 such indices were used. The research work concerned the observation of correlation between individual wavelengths and corresponding vegetation indexes. This paper seeks to emphasize the importance of particular wavelengths and spectral areas in crop scouting



  1. Simonovic, V, Markovic, D, Markovic, I, Mladenovic, G, & Ortopan, M 2017, “Impact of the sensor high in the measurement of the corn vegetative index”. ISAE 2017. The Third International Symposium on Agricultural Engineering, 20th-21st October 2017, Belgrade–Zemun, Serbia, sec.V, pp. 1-8
  2. Mukherjee, A, Narendra, SM & Raghuwanshi S 2019, “A survey of unmanned aerial sensing solutions in precision agriculture“, Journal of Network and Computer Applications, vol. 148.
  3. Srivastava, S, Bhutoria, AJ, Sharma, J, Sinha, A & Prem Pandey PC 2019, “UAVs technology for the development of GUI based application for precision agriculture and environmental research“, Remote Sensing Applications: Society and Environment, vol. 16.
  4. Simonović,V, Marković, D, Medojević, I, Joksimović, A & Tasić N. 2019, “Flight altitude of uas and overlap of images by multispectral camera optimization for crop scouting”, ISAE 2019. The 4th International Symposium on Agricultural Engineering, 31th October - 2nd November 2019, Belgrade–Zemun, Serbia, pp. 45-50
  5. Huang, W, Huang, J, Wang, X, Wang, F & Shi J 2013, “Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra”, Sensors, vol. 13, no. 12, pp. 16023–16050. Published online 2013 Nov 26.
  6. Cohen, JW 1988, “Statistical power analysis for the behavioral sciences“, Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 79-81.
  7. Jordan, CF 1969, “Derivation of leaf-area index from quality of light on the forest floor”, Ecology, vol. 50, no. 4, pp. 663–666.
  8. Chen, JM 1996, “Evaluation of vegetation indices and a modified simple ratio for boreal applications”, Canadian Journal of
  9. Remote Sensing, vol. 22, no. 3, pp. 229–242.
  10. Ide, R, Nakaji, T, Motohka, T & Oguma, H 2011, “Advantages of visible-band spectral remote sensing at both satellite and near-surface scales for monitoring the seasonal dynamics of GPP in a Japanese larch forest”, Journal of Agricultural Meteorology, vol. 67, no. 2, pp. 75–84.
  11. Xue, J & Su, B 2017, “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications”, Journal of Sensors, vol. 2017, pp. 1–17.
  12. Mokarram, M, Hojjati, M & Roshan, G 2015, “Modeling the behavior of Vegetation Indices in the salt dome of Korsia in North-East of Darab, Fars, Iran”, Modeling Earth Systems and Environment, vol. 1, no. 3, pp. 1-9.
  13. Esau, I, Miles, V, Davy, R, Miles, MW & Kurchatova A 2016, “Trends in normalized difference vegetation index (NDVI) associated with urban development in northern West Siberia”, Atmospheric Chemistry and Physics, vol. 16, no. 15, pp. 9563– 9577.
  14. Haboudane, D, Miller, JR, Pattey, E, Zarco-Tejada, PJ & Strachan, IB 2004, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture”, Remote Sensing of Environment, vol. 90, no. 3, pp. 337-352.
  15. Modzelewska, A, Stereńczak, K, Mierczyk, M, Maciuk, S, Balazy, R & Zawiła-Niedźwiecki T 2017, “Sensitivity of vegetation indices in relation to parameters of Norway spruce stands”, Folia Forestalia Polonica, Series A - Forestry, vol. 59, no. 2, pp. 85-98.
  16. Bannari, A, Asalhi, H & Teillet, PM 2002, “Transformed difference vegetation index (TDVI) for vegetation covermapping”, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS ’02, Toronto, Canada, 24-28 June 2002, pp. 3053-3055.
  17. McFeeters, SK 1996, “The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features“' , International Journal of Remote Sensing, vol. 17, no. 7, pp. 1425–1432.
  18. Steven, MD 1998, “The Sensitivity of the OSAVI Vegetation Index to Observational Parameters“, Remote Sensing of Environment, vol. 63, no. 1, pp. 49–60.

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