The DockWeeder robot enables organic dairy farming by controlling grassland

Project Details


Broad-leaved dock (Rumex obtusifolius L.) is a common and troublesome weed with a wide geographic distribution. The weed is readily consumed by livestock but its nutritive value is less than that of grass. The high contents of oxalic acid and oxalates can affect animal health if consumed in larger doses. When left uncontrolled, the weed will reach a high density and reduce grass yield by 10 to 40%. In conventional dairy farming, the weed is normally controlled by using herbicides. In organic farming no synthetic pesticides are used and there is a risk that broad-leaved dock will spread. This is also true in ecologically intensive dairy farming, where one of the goals is to maintain multispecies pastures where use of herbicides would affect desirable species such as clovers and vetch. As an illustration, on 17 organic dairy farms surveyed in The Netherlands, 51% of fields were infested at more than 1,000 plants hectare-1. Similarly, of 108 organic farmers surveyed in Germany, 85% indicated having problems with broad-leaved dock. Thus, broadleaved dock may turn out to be a serious obstacle to achieve the European goal of increasing the share of organic farming.

The solution proposed here consists of creating a robot that is capable of exploring a pasture by relying on GPS, equipping it with an array of sensors to detect the weed, and also equipping it with a non-chemical method to eliminate detected weeds. In earlier work, we demonstrated with an experimental robot that under certain conditions adequate weed detection and control is possible. Importantly, we found that the weed population remained low for three years after control. This earlier work had three major shortcomings: the mechanical construction of the autonomous platform was insufficiently robust, the weed detection worked only under a limited set of environmental conditions, and the weed control method was prone to mechanical breakdown on stony ground. In this project, we address all three shortcomings. We use an existing, robust autonomous platform, we advance the state of the art of weed detection by combining two-dimensional (2-D) and three-dimensional (3-D) imaging, and we adopt and optimize an innovative, environment-friendly hot-water treatment to eliminate weeds. In summary, by combining the expertise of the consortium partners, we will be able to build a robot to detect and control broad-leaved dock which has immediate commercial potential (TRL 7).
Short titleDockWeeder
Effective start/end date01/01/201631/12/2017

Research Output

  • 1 Journal article

Image-based recognition framework for robotic weed control systems

Kounalakis, T., Triantafyllidis, G. & Nalpantidis, L., Apr 2018, In : Multimedia Tools and Applications. 77, 8, p. 9567–9594 28 p.

Research output: Contribution to journalJournal articleResearchpeer-review

  • 7 Citations (Scopus)