Detecting Rumex Obtusifolius weed plants in grasslands from UAV RGB imagery using deep learning

J. Valente*, M. Doldersum, C. Roers, L. Kooistra

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

4 Citations (Scopus)

Abstract

Broad-leaved dock (Rumex obtusifolius) is a fast growing and spreading weed and is one of the most common weeds in production grasslands in the Netherlands. The heavy occurrence, fast growth and negative environmental-agricultural impact makes Rumex a species important to control. Current control is done directly in the field by mechanical or chemical actuation methods as soon as the plants are found in situ by the farmer. In nature conservation areas control is much more difficult because spraying is not allowed. This reduces the amount of grass and its quality. Rumex could be rapidly detected using high-resolution RGB images obtained from a UAV and optimize the plant control practices in wide nature conservation areas. In this paper, a novel approach for Rumex detection from orthomosaics obtained using a commercial available quadrotor (DJI Phantom 3 PRO) is proposed. The results obtained shown that Rumex can be detected up to 90% from a 6 mm/pixel ortho-mosaic generated from an aerial survey and using deep learning.

Original languageEnglish
Title of host publicationISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands
PublisherISPRS
Pages179-185
Number of pages7
DOIs
Publication statusPublished - 29 May 2019
Event4th ISPRS Geospatial Week 2019 - Enschede, Netherlands
Duration: 10 Jun 201914 Jun 2019

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
VolumeIV-2/W5
ISSN (Print)2194-9042

Conference

Conference4th ISPRS Geospatial Week 2019
CountryNetherlands
CityEnschede
Period10/06/1914/06/19

Keywords

  • Aerial surveying
  • Deep learning
  • DJI Phantom
  • Grasslands
  • Machine vision
  • Plant detection
  • Rumex
  • Weeding

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