Standard
Color Classification Methods for Perennial Weed Detection in Cereal Crops. / Forero, Manuel G.; Herrera-Rivera, Sergio; Ávila-Navarro, Julián; Franco, Camilo Andres; Rasmussen, Jesper; Nielsen, Jon.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. ed. / Ruben Vera-Rodriguez; Julian Fierrez; Aythami Morales. Springer, 2019. p. 117-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11401 LNCS). (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Vol. 11401).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Forero, MG, Herrera-Rivera, S, Ávila-Navarro, J, Franco, CA
, Rasmussen, J & Nielsen, J 2019,
Color Classification Methods for Perennial Weed Detection in Cereal Crops. in R Vera-Rodriguez, J Fierrez & A Morales (eds),
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11401 LNCS, Image Processing, Computer Vision, Pattern Recognition, and Graphics, vol. 11401, pp. 117-123, 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, Madrid, Spain,
19/11/2018.
https://doi.org/10.1007/978-3-030-13469-3_14APA
Forero, M. G., Herrera-Rivera, S., Ávila-Navarro, J., Franco, C. A.
, Rasmussen, J., & Nielsen, J. (2019).
Color Classification Methods for Perennial Weed Detection in Cereal Crops. In R. Vera-Rodriguez, J. Fierrez, & A. Morales (Eds.),
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings (pp. 117-123). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11401 LNCS Image Processing, Computer Vision, Pattern Recognition, and Graphics Vol. 11401
https://doi.org/10.1007/978-3-030-13469-3_14Vancouver
Forero MG, Herrera-Rivera S, Ávila-Navarro J, Franco CA
, Rasmussen J, Nielsen J.
Color Classification Methods for Perennial Weed Detection in Cereal Crops. In Vera-Rodriguez R, Fierrez J, Morales A, editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. Springer. 2019. p. 117-123. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11401 LNCS). (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Vol. 11401).
https://doi.org/10.1007/978-3-030-13469-3_14Author
Forero, Manuel G. ; Herrera-Rivera, Sergio ; Ávila-Navarro, Julián ; Franco, Camilo Andres ; Rasmussen, Jesper ; Nielsen, Jon. / Color Classification Methods for Perennial Weed Detection in Cereal Crops. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. editor / Ruben Vera-Rodriguez ; Julian Fierrez ; Aythami Morales. Springer, 2019. pp. 117-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11401 LNCS). (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Vol. 11401).
Bibtex
@inproceedings{9a0443ef7ddd49b9af0d649538fdc30a,
title = "Color Classification Methods for Perennial Weed Detection in Cereal Crops",
abstract = "Cirsium arvense is an invasive plant normally found in cold climates that affects cereal crops. Therefore, its detection is important to improve crop production. A previous study based on the analysis of aerial photographs focused on its detection using deep learning techniques and established methods based on image processing. This study introduces an image processing technique that generates even better results than those found with machine learning algorithms; this is reflected in aspects such as the accuracy and speed of the detection of the weeds in the cereal crops. The proposed method is based on the detection of the extreme green color characteristic of this plant with respect to the crops. To evaluate the technique, it was compared to six popular machine learning methods using images taken from two different heights: 10 and 50 m. The accuracy obtained with the machine learning techniques was 97.07% at best with execution times of more than 2 min with 200 × 200-pixel subimages, while the accuracy of the proposed image processing method was 98.23% and its execution time was less than 3 s.",
keywords = "Automated weed classification, Cereal crops, Deep learning, Image processing, Machine learning",
author = "Forero, {Manuel G.} and Sergio Herrera-Rivera and Juli{\'a}n {\'A}vila-Navarro and Franco, {Camilo Andres} and Jesper Rasmussen and Jon Nielsen",
year = "2019",
doi = "10.1007/978-3-030-13469-3_14",
language = "English",
isbn = "978-3-030-13468-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "117--123",
editor = "Ruben Vera-Rodriguez and Julian Fierrez and Aythami Morales",
booktitle = "Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications",
address = "Switzerland",
note = "23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 ; Conference date: 19-11-2018 Through 22-11-2018",
}
RIS
TY - GEN
T1 - Color Classification Methods for Perennial Weed Detection in Cereal Crops
AU - Forero, Manuel G.
AU - Herrera-Rivera, Sergio
AU - Ávila-Navarro, Julián
AU - Franco, Camilo Andres
AU - Rasmussen, Jesper
AU - Nielsen, Jon
PY - 2019
Y1 - 2019
N2 - Cirsium arvense is an invasive plant normally found in cold climates that affects cereal crops. Therefore, its detection is important to improve crop production. A previous study based on the analysis of aerial photographs focused on its detection using deep learning techniques and established methods based on image processing. This study introduces an image processing technique that generates even better results than those found with machine learning algorithms; this is reflected in aspects such as the accuracy and speed of the detection of the weeds in the cereal crops. The proposed method is based on the detection of the extreme green color characteristic of this plant with respect to the crops. To evaluate the technique, it was compared to six popular machine learning methods using images taken from two different heights: 10 and 50 m. The accuracy obtained with the machine learning techniques was 97.07% at best with execution times of more than 2 min with 200 × 200-pixel subimages, while the accuracy of the proposed image processing method was 98.23% and its execution time was less than 3 s.
AB - Cirsium arvense is an invasive plant normally found in cold climates that affects cereal crops. Therefore, its detection is important to improve crop production. A previous study based on the analysis of aerial photographs focused on its detection using deep learning techniques and established methods based on image processing. This study introduces an image processing technique that generates even better results than those found with machine learning algorithms; this is reflected in aspects such as the accuracy and speed of the detection of the weeds in the cereal crops. The proposed method is based on the detection of the extreme green color characteristic of this plant with respect to the crops. To evaluate the technique, it was compared to six popular machine learning methods using images taken from two different heights: 10 and 50 m. The accuracy obtained with the machine learning techniques was 97.07% at best with execution times of more than 2 min with 200 × 200-pixel subimages, while the accuracy of the proposed image processing method was 98.23% and its execution time was less than 3 s.
KW - Automated weed classification
KW - Cereal crops
KW - Deep learning
KW - Image processing
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85063068269&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-13469-3_14
DO - 10.1007/978-3-030-13469-3_14
M3 - Article in proceedings
AN - SCOPUS:85063068269
SN - 978-3-030-13468-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 117
EP - 123
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A2 - Vera-Rodriguez, Ruben
A2 - Fierrez, Julian
A2 - Morales, Aythami
PB - Springer
T2 - 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
Y2 - 19 November 2018 through 22 November 2018
ER -
