Which is the context of Dockweeder ?

1_Home_2_RumexBroad-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. 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 multi-species pastures where use of herbicides would affect desirable species such as clovers and vetch. 




What is the solution ?

The solution proposed here consists of creating a robot that is capable of exploring a pasture by relying o2_Project_1_PrototypeZurichn 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.


Materials and methods ?




The DockWeeder project will advance the state-of-the-art in automated detection and control of broad-leaved dock:

  • Detection of weeds will be improved by making use of 3-D imaging
  • Weeds will be controlled by treating them with hot water
  • A robust off-the-shelf autonomous vehicle will be fitted with weed detection and control







7 workpackages

DockWeeder project is divided in 7 workpackages and the work is organized in time as follows.

When user requirements have been made clear (month 3), WP3-4-5 proceed to a large extent in parallel.

At month 10, work on the autonomous vehicle and on the weed control method has progressed to the point where the two can be integrated.

At month 14, the weed detection system is specified and can be integrated on the robot (work on the weed detection algorithms will continue after this point in time).

At month 18, the work will focus entirely on integrating all components (navigation, detection, control) in one functioning system.