Color Classification Methods for Perennial Weed Detection in Cereal Crops

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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

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 languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings
EditorsRuben Vera-Rodriguez, Julian Fierrez, Aythami Morales
Publication date2019
ISBN (Print)978-3-030-13468-6
ISBN (Electronic)978-3-030-13469-3
Publication statusPublished - 2019
Event23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Spain
Duration: 19 Nov 201822 Nov 2018


Conference23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11401 LNCS
SeriesImage Processing, Computer Vision, Pattern Recognition, and Graphics

    Research areas

  • Automated weed classification, Cereal crops, Deep learning, Image processing, Machine learning

ID: 224337188