Color Classification Methods for Perennial Weed Detection in Cereal Crops

Manuel G. Forero; Sergio Herrera-Rivera; Julián Ávila-Navarro; Camilo Andres Franco; Jesper Rasmussen; Jon Nielsen

doi:10.1007/978-3-030-13469-3_14

Color Classification Methods for Perennial Weed Detection in Cereal Crops

Manuel G. Forero^*, Sergio Herrera-Rivera, Julián Ávila-Navarro, Camilo Andres Franco, Jesper Rasmussen, Jon Nielsen

^*Corresponding author af dette arbejde

Afgrødevidenskab

5 Citationer (Scopus)

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.

Originalsprog	Engelsk
Titel	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings
Redaktører	Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales
Forlag	Springer Verlag,
Publikationsdato	2019
Sider	117-123
ISBN (Trykt)	978-3-030-13468-6
ISBN (Elektronisk)	978-3-030-13469-3
DOI	https://doi.org/10.1007/978-3-030-13469-3_14
Status	Udgivet - 2019
Begivenhed	23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Spanien Varighed: 19 nov. 2018 → 22 nov. 2018

Konference

Konference	23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
Land/Område	Spanien
By	Madrid
Periode	19/11/2018 → 22/11/2018

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	11401 LNCS
ISSN	0302-9743

Navn	Image Processing, Computer Vision, Pattern Recognition, and Graphics
Vol/bind	11401

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10.1007/978-3-030-13469-3_14

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Citationsformater

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. I R. Vera-Rodriguez, J. Fierrez, & A. Morales (red.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings (s. 117-123). Springer Verlag,. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11401 LNCS Image Processing, Computer Vision, Pattern Recognition, and Graphics Bind 11401 https://doi.org/10.1007/978-3-030-13469-3_14

Color Classification Methods for Perennial Weed Detection in Cereal Crops. / Forero, Manuel G.; Herrera-Rivera, Sergio; Ávila-Navarro, Julián et al.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. red. / Ruben Vera-Rodriguez; Julian Fierrez; Aythami Morales. Springer Verlag, 2019. s. 117-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11401 LNCS). (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Bind 11401).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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. i R Vera-Rodriguez, J Fierrez & A Morales (red), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. Springer Verlag, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11401 LNCS, Image Processing, Computer Vision, Pattern Recognition, and Graphics, bind 11401, s. 117-123, 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, Madrid, Spanien, 19/11/2018. https://doi.org/10.1007/978-3-030-13469-3_14

Forero MG, Herrera-Rivera S, Ávila-Navarro J, Franco CA, Rasmussen J, Nielsen J. Color Classification Methods for Perennial Weed Detection in Cereal Crops. I Vera-Rodriguez R, Fierrez J, Morales A, red., Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. Springer Verlag,. 2019. s. 117-123. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11401 LNCS). (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Bind 11401). doi: 10.1007/978-3-030-13469-3_14

Forero, Manuel G. ; Herrera-Rivera, Sergio ; Ávila-Navarro, Julián et al. / 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. red. / Ruben Vera-Rodriguez ; Julian Fierrez ; Aythami Morales. Springer Verlag, 2019. s. 117-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11401 LNCS). (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Bind 11401).

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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",

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editor = "Ruben Vera-Rodriguez and Julian Fierrez and Aythami Morales",

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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

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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

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PB - Springer Verlag,

T2 - 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018

Y2 - 19 November 2018 through 22 November 2018

ER -

Color Classification Methods for Perennial Weed Detection in Cereal Crops

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