Design and comparative study of genetic algorithm optimized SVM (Support Vector Machines) configurations to classify crop/weed using shape/color features

WK Wong, and Muralindran Mariappan, and Ali Chekima, (2015) Design and comparative study of genetic algorithm optimized SVM (Support Vector Machines) configurations to classify crop/weed using shape/color features. International Journal of Computer Science and Electronics Engineering (IJCSEE), 3 (2). pp. 154-158. ISSN 2320–4028

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Abstract

This research work seeks to optimise classifiers to identify several types of weeds namely mixed Monocotyledon weeds , Agerantum Conyzoides (AGECO), Borreris Repens (BOIRE) and Brassica Juncea (BRSJU) for an selective automatic robotic sprayer. Tuning the parameters and selecting the feature for SVM requires extensive analysis on the features if performed

Item Type: Article
Uncontrolled Keywords: AGECO , BOIRE ,Monocotyledon weeds
Subjects: Q Science > QA Mathematics
Divisions: FACULTY > Faculty of Engineering
Depositing User: Unnamed user with email storage.bpmlib@ums.edu.my
Date Deposited: 09 Jan 2017 02:26
Last Modified: 25 Oct 2017 06:06
URI: http://eprints.ums.edu.my/id/eprint/15350

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