Journal
GIGASCIENCE
Volume 9, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/gigascience/giaa017
Keywords
precision agriculture; morphological operators; feature extraction; local binary patterns; contour masks; weed detection; computer vision
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Funding
- Grains Research and Development Corporation (GRDC)
- Photonic Detection Systems Pty. Ltd, Australia [WCA00004]
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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.
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