  The site-specific management of weeds in grassland is often challenging
because different weed control strategies have different trade-offs regarding
the required resources and treatment efficiency. So, the question arises
whether a wide tractor-based system with section control or a small
agricultural robot has a higher weed control performance for a given
infestation scenario. For example, a small autonomous robot moving from one
weed to the next might have much shorter travel distances (and thus lower
energy and time costs) than a tractor-mounted system if the locations of the
weeds are relatively isolated across the field. However, if the plants are
highly concentrated in small areas so-called clusters, the increased width of
the tractor-mounted implement could be beneficial because of shorter travel
distances and greater working width.
  An additional challenge is the fact that there is no complete knowledge of
the weed locations. Weeds may not have been detected, for example, due to their
growth stage, occlusion by other objects, or misclassification. Weed control
strategies must therefore also be evaluated with regard to this issue. Thus, in
addition to the driving distance, other metrics are also of interest, such as
the number of plants that were actually controlled or the size of the total
treatment area.
  We performed this investigation for the treatment of the toxic Colchicum
autumnale, which had been detected in drone images of extensive grassland
sites. In addition to real data, we generated and analyzed simulated weed
locations using mathematical models of stochastic geometry. These offer the
possibility to simulate very different spatial distributions of toxic plant
locations. Different treatment strategies were then virtually tested on this
data using Monte Carlo simulations and their performance was statistically
evaluated.
