Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model

Xinyu Hu; Fuzhong Li

doi:doi:10.11648/j.aff.20170601.17

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Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model

Xinyu Hu, Fuzhong Li

Published in Agriculture, Forestry and Fisheries (Volume 6, Issue 1)

Received: 13 March 2017 Published: 15 March 2017

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Abstract

RGB (Red-Green-Blue) model and HSV (Hue-Saturation-Value) model are used to classify corn seeding and weed. Plants are distinguished from background based on color feature, and the binary image is acquired. As foreground in binary image, corn seedling and weed are labelled effectively with a set of numbers after clearing noise and labelling connected components. Then saturation energy of each connected component with a certain label can be calculated, and the maximum saturation energy is corresponded to the region of corn seedling. Furthermore, in hue image, corn seedling root has larger hue value, so the location of root is acquired. Therefore, we can not only classify corn seedling and weed, but also acquire location of corn seedling root, and the results show that the method of classification corn seedling and weed based on RGB model and HSV model has a great accuracy and real-time performance.

Published in	Agriculture, Forestry and Fisheries (Volume 6, Issue 1)
DOI	10.11648/j.aff.20170601.17
Page(s)	49-54
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Connected Components Labeling, Saturation Energy, Classification, Corn Seedling, Location

References

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APA Style

Xinyu Hu, Fuzhong Li. (2017). Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model. Agriculture, Forestry and Fisheries, 6(1), 49-54. https://doi.org/10.11648/j.aff.20170601.17

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ACS Style

Xinyu Hu; Fuzhong Li. Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model. Agric. For. Fish. 2017, 6(1), 49-54. doi: 10.11648/j.aff.20170601.17

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AMA Style

Xinyu Hu, Fuzhong Li. Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model. Agric For Fish. 2017;6(1):49-54. doi: 10.11648/j.aff.20170601.17

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@article{10.11648/j.aff.20170601.17,
  author = {Xinyu Hu and Fuzhong Li},
  title = {Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {6},
  number = {1},
  pages = {49-54},
  doi = {10.11648/j.aff.20170601.17},
  url = {https://doi.org/10.11648/j.aff.20170601.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20170601.17},
  abstract = {RGB (Red-Green-Blue) model and HSV (Hue-Saturation-Value) model are used to classify corn seeding and weed. Plants are distinguished from background based on color feature, and the binary image is acquired. As foreground in binary image, corn seedling and weed are labelled effectively with a set of numbers after clearing noise and labelling connected components. Then saturation energy of each connected component with a certain label can be calculated, and the maximum saturation energy is corresponded to the region of corn seedling. Furthermore, in hue image, corn seedling root has larger hue value, so the location of root is acquired. Therefore, we can not only classify corn seedling and weed, but also acquire location of corn seedling root, and the results show that the method of classification corn seedling and weed based on RGB model and HSV model has a great accuracy and real-time performance.},
 year = {2017}
}

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TY  - JOUR
T1  - Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model
AU  - Xinyu Hu
AU  - Fuzhong Li
Y1  - 2017/03/15
PY  - 2017
N1  - https://doi.org/10.11648/j.aff.20170601.17
DO  - 10.11648/j.aff.20170601.17
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 49
EP  - 54
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20170601.17
AB  - RGB (Red-Green-Blue) model and HSV (Hue-Saturation-Value) model are used to classify corn seeding and weed. Plants are distinguished from background based on color feature, and the binary image is acquired. As foreground in binary image, corn seedling and weed are labelled effectively with a set of numbers after clearing noise and labelling connected components. Then saturation energy of each connected component with a certain label can be calculated, and the maximum saturation energy is corresponded to the region of corn seedling. Furthermore, in hue image, corn seedling root has larger hue value, so the location of root is acquired. Therefore, we can not only classify corn seedling and weed, but also acquire location of corn seedling root, and the results show that the method of classification corn seedling and weed based on RGB model and HSV model has a great accuracy and real-time performance.
VL  - 6
IS  - 1
ER  -

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Author Information

Xinyu Hu

Department of Software, Shanxi Agriculture University, Taigu, China
Fuzhong Li

Department of Software, Shanxi Agriculture University, Taigu, China

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APA Style

Xinyu Hu, Fuzhong Li. (2017). Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model. Agriculture, Forestry and Fisheries, 6(1), 49-54. https://doi.org/10.11648/j.aff.20170601.17

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ACS Style

Xinyu Hu; Fuzhong Li. Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model. Agric. For. Fish. 2017, 6(1), 49-54. doi: 10.11648/j.aff.20170601.17

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AMA Style

Xinyu Hu, Fuzhong Li. Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model. Agric For Fish. 2017;6(1):49-54. doi: 10.11648/j.aff.20170601.17

Copy | Download

@article{10.11648/j.aff.20170601.17,
  author = {Xinyu Hu and Fuzhong Li},
  title = {Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {6},
  number = {1},
  pages = {49-54},
  doi = {10.11648/j.aff.20170601.17},
  url = {https://doi.org/10.11648/j.aff.20170601.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20170601.17},
  abstract = {RGB (Red-Green-Blue) model and HSV (Hue-Saturation-Value) model are used to classify corn seeding and weed. Plants are distinguished from background based on color feature, and the binary image is acquired. As foreground in binary image, corn seedling and weed are labelled effectively with a set of numbers after clearing noise and labelling connected components. Then saturation energy of each connected component with a certain label can be calculated, and the maximum saturation energy is corresponded to the region of corn seedling. Furthermore, in hue image, corn seedling root has larger hue value, so the location of root is acquired. Therefore, we can not only classify corn seedling and weed, but also acquire location of corn seedling root, and the results show that the method of classification corn seedling and weed based on RGB model and HSV model has a great accuracy and real-time performance.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model
AU  - Xinyu Hu
AU  - Fuzhong Li
Y1  - 2017/03/15
PY  - 2017
N1  - https://doi.org/10.11648/j.aff.20170601.17
DO  - 10.11648/j.aff.20170601.17
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 49
EP  - 54
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20170601.17
AB  - RGB (Red-Green-Blue) model and HSV (Hue-Saturation-Value) model are used to classify corn seeding and weed. Plants are distinguished from background based on color feature, and the binary image is acquired. As foreground in binary image, corn seedling and weed are labelled effectively with a set of numbers after clearing noise and labelling connected components. Then saturation energy of each connected component with a certain label can be calculated, and the maximum saturation energy is corresponded to the region of corn seedling. Furthermore, in hue image, corn seedling root has larger hue value, so the location of root is acquired. Therefore, we can not only classify corn seedling and weed, but also acquire location of corn seedling root, and the results show that the method of classification corn seedling and weed based on RGB model and HSV model has a great accuracy and real-time performance.
VL  - 6
IS  - 1
ER  -

Copy | Download