Abstract
Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the "bccr-segset" dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. Conclusions: The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection. © 2020 The Author(s) 2020.
RAS ID
34086
Document Type
Journal Article
Date of Publication
1-1-2020
Funding Information
Photonic Detection Systems, PDS
Grains Research and Development Corporation, GRDC
School
Electron Science Research Institute
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
Oxford University Press
Identifier
Vi Nguyen Thanh Le
ORCID: 0000-0003-2665-9332
Comments
Le, V. N. T., Ahderom, S., Apopei, B., & Alameh, K. (2020). A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators. GigaScience, 9(3), Article giaa017. https://doi.org/10.1093/gigascience/giaa017