As highly adaptable plants, weeds have evolved numerous mechanisms to evade control in agroecosystems. For example, reliance on herbicides has resulted in widespread evolution of resistance in many species. Minimising weed adaptation is a major driver for integrated weed management strategies. Crop mimicry is a notable example of weed adaptation, where weed species evolve to avoid control by mimicking aspects of the crop phenotype. Visual selection by hand weeding has been documented to select for crop mimics that are difficult to distinguish from the crop at the vegetative stage. With recent advancements in weed recognition technologies, image-based weed recognition for in-crop, site-specific weed control is on the cusp of becoming widely adopted. Whilst the control methods used in site-specific weed control will be varied (e.g., spot spraying or lasers), they will share weed recognition technology. Visual selection via image-based deep learning represents a selection pressure for weeds that can evade detection by mimicking crops. This mimicry may reduce weed recognition accuracy and thus weed control efficacy over time and result in difficult to manage mimetic weed phenotypes. Therefore, it is timely to explore the potential for selection of crop mimics by image-based weed recognition algorithms.