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• W3010358965 abstract "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." @default.
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• W3010358965 date "2020-03-01" @default.
• W3010358965 modified "2023-10-02" @default.
• W3010358965 title "A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators" @default.
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• W3010358965 doi "https://doi.org/10.1093/gigascience/giaa017" @default.
• W3010358965 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7055473" @default.
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