Most high-yielding crops are susceptible to abiotic and biotic stresses, making them particularly vulnerable to the potential effects of climate change. A possible alternative is to accelerate the domestication of wild plants that are already tolerant to harsh conditions and to increase their yields by methods such as gene editing. We foresee that crops’ wild progenitors could potentially compete with the resulting de novo domesticated plants, reducing yields. To improve the recognition of weeds, we propose using gene editing techniques to introduce traits into de novo domesticated crops that will allow for visual recognition of the crops by weeding robots that have been trained by machine learning.