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

  • Pradip Salve
  • Pravin YannawarEmail author
  • Milind SardesaiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

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.

Keywords

Leaf veins Plant recognition Plant classification Leaf features 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Vision and Intelligence Lab, Department of Computer Science and ITDr. Babasaheb Ambedkar Marathwada UniversityAurangabadIndia
  2. 2.Floristic Research Lab, Department of BotanySavitribai Phule Pune UniversityPuneIndia

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