Color Classification Methods for Perennial Weed Detection in Cereal Crops

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 Verlag,

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

Y2 - 19 November 2018 through 22 November 2018

ER -