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Color Classification Methods for Perennial Weed Detection in Cereal Crops

  • Manuel G. ForeroEmail author
  • Sergio Herrera-Rivera
  • Julián Ávila-Navarro
  • Camilo Andres Franco
  • Jesper Rasmussen
  • Jon Nielsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

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 Machine learning Deep learning Image processing Cereal crops 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Facultad de IngenieríaUniversidad de IbaguéIbaguéColombia
  2. 2.Departamento de Ingeniería IndustrialUniversidad de los AndesBogotáColombia
  3. 3.Department of Plant and Environmental SciencesUniversity of CopenhagenCopenhagenDenmark

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