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Parametric Model for Flora Detection in Middle Himalayas

Author

Listed:
  • Aviral Sharma

    (Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India)

  • Saumya Nigam

    (University of Petroleum and Energy Studies, India)

Abstract

Plant detection forms an integral part of the life of the forest guards, researchers, and students in the field of botany and for common people also who are curious about knowing a plant. But detecting plants suffer a major drawback that the true identifier is only the flower, and in certain species, flowering occurs at major time period gaps spanning from few months to over 100 years (in certain types of bamboos). Machine learning-based systems could be used in developing models where the experience of researchers in the field of plant sciences can be incorporated into the model. In this paper, the authors present a machine learning-based approach based upon other quantifiable parameters for the detection of the plant presented. The system takes plant parameters as the inputs and will detect the plant family as the output.

Suggested Citation

  • Aviral Sharma & Saumya Nigam, 2022. "Parametric Model for Flora Detection in Middle Himalayas," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 14(1), pages 1-11, January.
    • Handle: RePEc:igg:jdsst0:v:14:y:2022:i:1:p:1-11
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDSST.286698
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