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Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1199))

Abstract

Sentinel-2 optical time-series images obtained at high resolution are creditable for cropland mapping which is the key for sustainable agriculture. The presented work was conducted in a heterogeneous region in Sameerwadi with an aim to classify sugarcane crops, with mainly two groups so as to provide a sugarcane field map, using Sentinel-2 normalized difference vegetation index (NDVI) time-series data. The potential of two better-known machine learning (ML) classifiers, random forest (RF) and support vector machine (SVM), was investigated to identify seven classes including sugarcane, early sugarcane, maize, waterbody, fallow land, built-up and bare land, and a sugarcane crop map is produced. Both the classifiers were able to effectively classify sugarcane areas and other land covers from the time-series data. Our results show that RF achieved higher overall accuracy (88.61%) than SVM having an overall accuracy of 81.86%. This study demonstrated that utilizing the Sentinel-2 NDVI time-series with RF and SVM successfully classified sugarcane crop fields.

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Acknowledgments

The authors would like to thank the staff of KIAAR and GBL, Sameerwadi, Karnataka, India, for their support and efforts in collecting ground truth data of crop plots used as the training set in this study.

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Authors and Affiliations

  1. Department of Computer Engineering & IT, College of Engineering, Pune, SPPU, India

    Shyamal Virnodkar & V. K. Pachghare

  2. K. J. Somaiya Institute of Applied Agricultural Research, Saidapur, Karnataka, India

    V. C. Patil & Sunil Kumar Jha

Authors
  1. Shyamal Virnodkar
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  2. V. K. Pachghare
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  3. V. C. Patil
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  4. Sunil Kumar Jha
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Corresponding author

Correspondence to Shyamal Virnodkar .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, India

    Bibudhendu Pati

  3. Department of Computer Science, University of California, Davis, CA, USA

    Prasant Mohapatra

  4. Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Virnodkar, S., Pachghare, V.K., Patil, V.C., Jha, S.K. (2021). Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_15

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  • DOI: https://doi.org/10.1007/978-981-15-6353-9_15

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  • Online ISBN: 978-981-15-6353-9

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