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Reseach Article

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

by Honse-Al-Walid, Md. Tanjim-Al-Akib, Nusratul Islam
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 3
Year of Publication: 2017
Authors: Honse-Al-Walid, Md. Tanjim-Al-Akib, Nusratul Islam
10.5120/cae2017652610

Honse-Al-Walid, Md. Tanjim-Al-Akib, Nusratul Islam . Feature Optimization of Whitefly Detection Algorithm using Image Segmentation and Feature Analysis. Communications on Applied Electronics. 7, 3 ( Jun 2017), 6-13. DOI=10.5120/cae2017652610

@article{ 10.5120/cae2017652610,
author = { Honse-Al-Walid, Md. Tanjim-Al-Akib, Nusratul Islam },
title = { Feature Optimization of Whitefly Detection Algorithm using Image Segmentation and Feature Analysis },
journal = { Communications on Applied Electronics },
issue_date = { Jun 2017 },
volume = { 7 },
number = { 3 },
month = { Jun },
year = { 2017 },
issn = { 2394-4714 },
pages = { 6-13 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number3/741-2017652610/ },
doi = { 10.5120/cae2017652610 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:01:22.164870+05:30
%A Honse-Al-Walid
%A Md. Tanjim-Al-Akib
%A Nusratul Islam
%T Feature Optimization of Whitefly Detection Algorithm using Image Segmentation and Feature Analysis
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 3
%P 6-13
%D 2017
%I Foundation of Computer Science (FCS), NY, 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.

References
  1. Kwadwo, Asenso-Okyere, K. Davis, and D. Aredo, “Advancing agriculture in developing countries through knowledge and innovation,” Synopsis of an international conference. International Food Policy Research Institute (with the German Agency for Technical Cooperation (GTZ) among others) in Addis Abeba. 2008.
  2. Chakraborty, Sukumar, and Adrian C. Newton, “Climate change, plant diseases and food security: an overview,” Plant Pathology 60.1 (2011): 2-14.
  3. Rasdi, Mohd Z., et al., “Population ecology of whitefly, Bemisia tabaci, (Homoptera: Aleyrodidae) on brinjal,” Journal of Agricultural Science 1.1 (2009): 27.
  4. Boissard, Paul, Vincent Martin, and Sabine Moisan, “A cognitive vision approach to early pest detection in greenhouse crops,” computers and electronics in agriculture 62.2 (2008): 81-93.
  5. S. P. Bhamare and S. C. Kulkarni, “Detection of Black Sigatoka on Banana Tree using Image Processing Techniques,” IOSR Journal of Electronics and Communication Engineering, pp. 60-65.
  6. Mundada, Rupesh G., and V. V. Gohokar, “Detection and classification of pests in greenhouse using image processing.” IOSR Journal of Electronics and Communication Engineering 5.6 (2013): 57-63.
  7. Shannon, Claude Elwood, “A mathematical theory of communication,” ACM SIGMOBILE Mobile Computing and Communications Review 5.1 (2001): 3-55
  8. Bland, J. Martin, and Douglas G. Altman, “ Statistics notes: measurement error, ” Bmj 313.7059 (1996): 744.
  9. Peli, Eli, “Contrast in complex images,” JOSA A 7.10 (1990): 2032- 2040.
  10. Pearson, Karl, “Note on regression and inheritance in the case of two parents,” Proceedings of the Royal Society of London 58 (1895): 240- 242.
  11. Janecek, Andreas, et al., “On the Relationship Between Feature Selection and Classification Accuracy,” FSDM. 2008.
  12. Peli, Eli, “Contrast in complex images,” JOSA A 7.10 (1990): 2032- 2040.
  13. Pearson, Karl, “Note on regression and inheritance in the case of two parents,” Proceedings of the Royal Society of London 58 (1895): 240- 242.
  14. Pham, Dzung L, Chenyang Xu, and Jerry L. Prince. “Current methods in medical image segmentation 1,” Annual review of biomedical engineering 2.1 (2000): 315-337.
  15. N. Ray, “Digital Image Basics: File Formats,” in CMPUT, 2010.
  16. Kumar, Tarun, and Karun Verma, “ A Theory Based on Conversion of RGB image to Gray image, ” International Journal of Computer Applications 7.2 (2010): 7-10.
  17. Bay, Herbert, Tinne Tuytelaars, and Luc Van Gool, “Surf: Speeded up robust features,” European conference on computer vision. Springer Berlin Heidelberg, 2006
  18. Pearson, Karl, “Contributions to the mathematical theory of evolution,” Philosophical Transactions of the Royal Society of London. A 185 (1894): 71-110.
  19. Burges, Christopher JC, “A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery 2.2 (1998): 121-167
Index Terms

Computer Science
Information Sciences

Keywords

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