Supporting data for "A novel k-FLBPCM method for detecting morphologically similar crops and weeds based on the combination of contour masks and Local Binary Pattern operators"

Dataset type: Imaging, Software
Data released on February 06, 2020

Le VNT; Ahderom S; Apopei B; Alameh K (2020): Supporting data for "A novel k-FLBPCM method for detecting morphologically similar crops and weeds based on the combination of contour masks and Local Binary Pattern operators" GigaScience Database. http://dx.doi.org/10.5524/100708

DOI10.5524/100708

Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops making them difficult to discriminate. This paper proposes 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 four 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 four classes in the “bccr-segset” dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method.





Sample IDTaxonomic IDCommon NameGenbank NameScientific NameSample Attributes
Canola_001138011annual rape Brassica napus var. napus Description:Image of Canola leaves captured using ...
Pixel resolution:1mm
Sample storage location:ESRI (Electron Science Res...
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Corn_003381124maysZea mays sub sp. Mays Description:Image of Corn leaves captured using a ...
Pixel resolution:1mm
Sample storage location:ESRI (Electron Science Res...
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Radish_0023726 radishRaphanus sativus Description:Image of Radish leaves captured using ...
Pixel resolution:1mm
Sample storage location:ESRI (Electron Science Res...
...
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Displaying 1-3 of 3 Sample(s).




File NameSample IDData TypeFile FormatSizeRelease Date 
ImageTAR25.6 MB2020-02-02
Imagearchive308.64 MB2020-02-02
Imagearchive1.29 GB2020-02-02
Canola_001ImageTAR65.15 MB2020-02-02
Canola_001ImageTAR65.47 MB2020-02-02
Corn_003ImageTAR56.63 MB2020-02-02
GitHub archivearchive528.45 KB2020-02-02
Radish_002ImageTAR67.44 MB2020-02-02
Radish_002ImageTAR66.92 MB2020-02-02
ReadmeTEXT3 KB2020-02-02
Displaying 1-10 of 10 File(s).
Funding body Awardee Award ID Comments
Australian Research Council Kamal Alameh WCA00004
Date Action
February 6, 2020 Dataset publish
March 16, 2020 Manuscript Link added : 10.1093/gigascience/giaa017
October 7, 2022 Manuscript Link updated : 10.1093/gigascience/giaa017