Herbicide resistant weeds are becoming a problem in agriculture, causing millions of dollars’ worth of losses each year. A possible approach to mechanical control of weeds would be a team of small robots that are able to weed a given field in an algorithmic fashion. This research provides a suitable simulation environment to model weed growth and a way to count weeds in video frames collected by RGB cameras onboard the robots to estimate weed density, both of which will help develop a data driven predictor that can estimate weed growth in a field given an initial seed bank density, in the interest of creating a coordinated weeding policy
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