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2021 | OriginalPaper | Chapter

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

Authors : Gayatri Pattnaik, K. Parvathi

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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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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Metadata
Title
Automatic Detection and Classification of Tomato Pests Using Support Vector Machine Based on HOG and LBP Feature Extraction Technique
Authors
Gayatri Pattnaik
K. Parvathi
Copyright Year
2021
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021

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