Automatic detection of thistle-weeds in cereal crops from aerial RGB images

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

  • Camilo Franco
  • Carely Guada
  • J. Tinguaro Rodríguez
  • Jon Nielsen
  • Rasmussen, Jesper
  • Daniel Gómez
  • Javier Montero

Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50 m above the ground, with the purpose of applying herbicide for site-specific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.

Original languageEnglish
Title of host publicationInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings
Number of pages12
PublisherSpringer
Publication date2018
Pages441-452
ISBN (Print)9783319914787
DOIs
Publication statusPublished - 2018
Event17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018 - Cadiz, Spain
Duration: 11 Jun 201815 Jun 2018

Conference

Conference17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018
LandSpain
ByCadiz
Periode11/06/201815/06/2018
SeriesCommunications in Computer and Information Science
Volume855
ISSN1865-0929

    Research areas

  • Image analysis, k-Nearest neighbours, Neural networks, Precision agriculture, Soft measures, Weed detection

ID: 201043385