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Evaluation and Analysis of Plant Classification System Based on Feature Level Fusion and Score Level Fusion

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

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Abstract

This paper describes the automatic leaf recognition based on feature level fusion and score level fusion of vein orientation angles, GLCM, SIFT, SURF as a features. However, to obtain the sophisticated leaf recognition, the system must be undergo through numerous difficulties such as intra and inter-class variations in plants and defining the proper local and global image descriptors which can deal with the color, shape and textual, information for the classification. Selection of the meticulous features plays key role in designing the best classification system. In this paper we proposed multi-modal plant classification where several components are fused together for a more precise classification. The results shows that the proposed system for feature level fusion achieved 93.72% GAR with 6.27% of EER and for score level fusion system achieves 97.13% GAR and 2.86% EER. It is found that the performance of the classification has been increased by 3.79% of EER when score level fusion applied to the system.

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Acknowldgment

The authors would like to acknowledge Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India providing support for the infrastructure during the research work and UGC-MANF fellowship for financial support.

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Authors and Affiliations

  1. Vision and Intelligence Lab, Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, MS, India

    Pradip Salve & Pravin Yannawar

  2. Floristic Research Lab, Department of Botany, Savitribai Phule Pune University, Pune, India

    Milind Sardesai

Authors
  1. Pradip Salve
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  2. Pravin Yannawar
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  3. Milind Sardesai
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Corresponding authors

Correspondence to Pravin Yannawar or Milind Sardesai .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Salve, P., Yannawar, P., Sardesai, M. (2019). Evaluation and Analysis of Plant Classification System Based on Feature Level Fusion and Score Level Fusion. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_41

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

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