Greenness identification using visible spectral colour indices for site specific weed management

Abstract

In the present study an attempt has been made to identify the green vegetation based on colour using visible spectral colour indices such as excess green index (ExG), excess red index (ExR) and excess green minus excess red index (ExGR). At first stage, the performance of colour indices were tested at four illumination intensities using the standard colour patches. The results indicated a clear separation between the ExG, ExR and ExGR values of green colour patches (foliage, yellow green & green) and soil colour patches (dark skin, moderate red & magenta) at illumination intensity of 89.04 ± 8.12 lux than 188.8 ± 6.36, 259.25 ± 12.73 and 359.28 ± 10.10 lux illumination intensities. This observation suggested that the colour indices might perform better at low lighting condition. In the second stage, the images of original plants and soil were captured at an illumination intensity of 89.04 ± 8.12 lux and classification rate at different threshold were studied. The average correct classification rate of ExGR and ExG colour indices were found to be 93.03% and 86.03% at threshold values 0 and 10, respectively. This indicates that the colour index ExGR could be successfully employed for image based classification of plant and non-plant material.

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Acknowledgements

The authors thank Director, ICAR-CIAE, Bhopal, for providing the facilities to conduct experiments.

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UK: Conceptualization, Methodology, Data curation, Software, Writing- Original draft preparation. KNA: Conceptualization, Methodology, Reviewing and Editing. NSC: Laboratory facility, Reviewing and Editing. KS: Software, Reviewing and Editing.

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Correspondence to K. Upendar.

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Upendar, K., Agrawal, K.N., Chandel, N.S. et al. Greenness identification using visible spectral colour indices for site specific weed management. Plant Physiol. Rep. (2021). https://doi.org/10.1007/s40502-020-00562-0

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Keywords

  • Colour indices
  • Image processing
  • Image segmentation
  • Imaging sensor
  • Machine vision
  • Weed identification