Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/10096
Title: | A Study on Local Binary Pattern for Automated Weed Classification Using Template Matching and Support Vector Machine |
---|---|
Contributor(s): | Ahmed, Faisal (author); Hossain Bari, ASM (author); Shihavuddin, ASM (author); Al-Mamun, Hawlader A (author); Kwan, Paul H (author) |
Publication Date: | 2011 |
DOI: | 10.1109/CINTI.2011.6108524 |
Handle Link: | https://hdl.handle.net/1959.11/10096 |
Abstract: | Concerns regarding the environmental and economic impacts of excessive herbicide applications in agriculture have promoted interests in seeking alternative weed control strategies. In this context, an automated machine vision system that has the ability to differentiate between broadleaf and grass weeds in digital images to optimize the selection and dosage of herbicides can enhance the profitability and lessen environmental degradation. This paper presents an efficient and effective texture-based weed classification method using local binary pattern (LBP). The objective was to evaluate the feasibility of using micro-level texture patterns to classify weed images into broadleaf and grass categories for real-time selective herbicide applications. Two well-known machine learning methods, template matching and support vector machine, are used for classification. Experiments on 200 sample field images with 100 samples from each category show that, the proposed method is capable of classifying weed images with high accuracy and computational efficiency. |
Publication Type: | Conference Publication |
Conference Details: | CINTI 2011: 12th IEEE International Symposium on Computational Intelligence and Informatics, Budapest, Hungary, 21st - 22nd November, 2011 |
Source of Publication: | Proceedings of the 12th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), p. 329-334 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Place of Publication: | Los Alamitos, United States of America |
Fields of Research (FoR) 2008: | 080106 Image Processing 080109 Pattern Recognition and Data Mining 080104 Computer Vision |
Socio-Economic Objective (SEO) 2008: | 890201 Application Software Packages (excl. Computer Games) 899899 Environmentally Sustainable Information and Communication Services not elsewhere classified 960499 Control of Pests, Diseases and Exotic Species not elsewhere classified |
Peer Reviewed: | Yes |
HERDC Category Description: | E1 Refereed Scholarly Conference Publication |
Appears in Collections: | Conference Publication |
Files in This Item:
File | Description | Size | Format |
---|
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.