Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12313/926
Title: Color Classification Methods for Perennial Weed Detection in Cereal Crops
Authors: Forero Vargas, Manuel Guillermo
Herrera-Rivera, Sergio
Ávila-Navarro, Julián
Franco, Camilo Andres
Rasmussen, Jesper
Nielsen, Jon
Keywords: Automated weed classification
Machine learning
Deep learning
Image processing
Cereal crops
Issue Date: 3-Mar-2019
Publisher: Lecture Notes in Computer Science
Citation: 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: Vera-Rodriguez R., Fierrez J., Morales A. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2018. Lecture Notes in Computer Science, vol 11401. Springer, Cham
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.
URI: https://link.springer.com/chapter/10.1007/978-3-030-13469-3_14
ISSN: 0302-9743
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