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Automatic Detection and Classification of Tomato Pests Using Support Vector Machine Based on HOG and LBP Feature Extraction Technique

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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

The automatic detection and classification of insect pest is emerged as one of the interesting research areas in agriculture sector to ensure reduction of damages due to pest. From the general process of detection of pest, feature extraction plays a significant role. It extracts features from the segmented image obtained by segmentation process, and then extracted images are being transferred to a classifier for the operations. In this work, we studied and implemented two feature extraction techniques, i.e., Histogram of Oriented Gradient (HOG) and Local Binary Pattern techniques (LBP). The comparison result expressed that HOG performs better than its counterpart. The result comes with accuracy of 97% for HOG. Here, we are adopting SVM-based pest classification as a test case.

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Authors and Affiliations

  1. KIIT Deemed to be University, Bhubaneswar, India

    Gayatri Pattnaik & K. Parvathi

Authors
  1. Gayatri Pattnaik
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  2. K. Parvathi
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Corresponding author

Correspondence to Gayatri Pattnaik .

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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Pattnaik, G., Parvathi, K. (2021). Automatic Detection and Classification of Tomato Pests Using Support Vector Machine Based on HOG and LBP Feature Extraction Technique. 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_5

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  • DOI: https://doi.org/10.1007/978-981-15-6353-9_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6352-2

  • Online ISBN: 978-981-15-6353-9

  • eBook Packages: EngineeringEngineering (R0)

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