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 for this work

Section for Crop Sciences

5 Citations (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.

Original language	English
Title of host publication	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings
Editors	Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales
Publisher	Springer Verlag,
Publication date	2019
Pages	117-123
ISBN (Print)	978-3-030-13468-6
ISBN (Electronic)	978-3-030-13469-3
DOIs	https://doi.org/10.1007/978-3-030-13469-3_14
Publication status	Published - 2019
Event	23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Spain Duration: 19 Nov 2018 → 22 Nov 2018

Conference

Conference	23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
Country/Territory	Spain
City	Madrid
Period	19/11/2018 → 22/11/2018

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11401 LNCS
ISSN	0302-9743

Series	Image Processing, Computer Vision, Pattern Recognition, and Graphics
Volume	11401

Keywords

Automated weed classification
Cereal crops
Deep learning
Image processing
Machine learning

Access to Document

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

Cite this

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 Verlag,. 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. ed. / Ruben Vera-Rodriguez; Julian Fierrez; Aythami Morales. Springer Verlag, 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

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 Verlag, 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_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. 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 Verlag,. 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). 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. editor / Ruben Vera-Rodriguez ; Julian Fierrez ; Aythami Morales. Springer Verlag, 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).

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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",

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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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ER -

Color Classification Methods for Perennial Weed Detection in Cereal Crops

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