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Feature Optimization of Whitefly Detection Algorithm using Image Segmentation and Feature Analysis

Honse-Al-Walid, Md. Tanjim-Al-Akib, Nusratul Islam. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Honse-Al-Walid, Md. Tanjim-Al-Akib, Nusratul Islam
10.5120/cae2017652610

Honse-Al-Walid, Md. Tanjim-Al-Akib and Nusratul Islam. Feature Optimization of Whitefly Detection Algorithm using Image Segmentation and Feature Analysis. Communications on Applied Electronics 7(3):6-13, June 2017. BibTeX

@article{10.5120/cae2017652610,
	author = {Honse-Al-Walid and Md. Tanjim-Al-Akib and Nusratul Islam},
	title = {Feature Optimization of Whitefly Detection Algorithm using Image Segmentation and Feature Analysis},
	journal = {Communications on Applied Electronics},
	issue_date = {June 2017},
	volume = {7},
	number = {3},
	month = {Jun},
	year = {2017},
	issn = {2394-4714},
	pages = {6-13},
	numpages = {8},
	url = {http://www.caeaccess.org/archives/volume7/number3/741-2017652610},
	doi = {10.5120/cae2017652610},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

With the increase of agricultural production worldwide, the economic importance of early detection of crop pests is more evident than ever before. The purpose of this study is to improve the performance of crop pest detection algorithm using image processing techniques. This study combines the object extraction, feature extraction and classification of a particular crop pest species using the SVM classifier. The end point of this study is to achieve optimal performance and accuracy.

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Keywords

Image Processing, Image Segmentation, Feature Extraction, Pest Detection, Whitefly, Blob Extraction, SVM Classifier, Performance Analysis