Weeds are a persistent and significant problem in agricultural production. Weeds, which tend to grow and produce very rapidly, compete with crops for critical resources, which can significantly reduce crop yields (Zimdahl, 2007). Herbicides are one of the most common and inexpensive approaches to controlling weeds; however, their continued and widespread use has prompted concerns due to off-target movement and steadily rising herbicide-resistant weed populations (Westwood et al., 2018). Given the significance of increasing herbicide-resistant weed populations and economic pressures to reduce costs associated with weeding, there is a need to implement more sustainable weed management approaches. Integrated weed management (IWM) is an approach that combines multiple tactics, including genetic, biological, chemical, ecological, and mechanical approaches, for controlling weeds (Harker and O’Donovan, 2013; Pittman et al., 2020). The principle of IWM suggests that any one of these approaches, when used alone, will not result in optimized weed control; instead, development and application of multiple tactics is necessary. Further, Young (2018) argues that IWM is a continuum, and that “true IWM” requires integrating plant ecological and biological knowledge with technological machinery and algorithm-based decision making to respond to changes in weeds and the environment. To achieve this precise and specific integrated weed management paradigm, autonomous weeding systems will likely play a critical role (Young et al., 2017).