In a recent study published in the journal Trends in Plant Science, researchers propose using bioengineering techniques to make weeding easier by altering the genomes of crops to be visually distinctive from their wild counterparts. By introducing traits such as pigments like anthocyanins or carotenoids that are already produced by many plants, crops could be easily distinguished by weeding robots trained through machine learning. This approach combines genome editing with artificial intelligence to create a sustainable method for eliminating wild analogues in agricultural fields.
Traditional crop domestication involved meticulous breeding and selection over thousands of years, resulting in the desirable traits we see in modern crops today. With the advancement of genetics, scientists have identified the genes responsible for these traits, making it possible to rapidly domesticate new crops through bioengineering techniques like gene editing. By introducing these traits into wild plants, new crops could be created that are more climate change-resistant and environmentally sustainable, ultimately leading to higher yields and eco-friendly agricultural practices.
De novo domesticated crops may closely resemble their wild counterparts, making it difficult to differentiate them for weeding purposes. Instead of relying on herbicides, the researchers suggest genetically engineering crops to express pigments that are commonly found in plants, such as anthocyanins and carotenoids. These pigments not only facilitate visual recognition but also have additional benefits for plant and human health, such as increased resistance to environmental stresses and serving as a source of nutrients in the human diet.
By manipulating key genes responsible for pigment production, researchers believe they can enhance the accuracy of distinguishing newly domesticated crops from weeds. Additionally, altering the leaf structure of these crops or changing the color or shape of their seeds could further facilitate weeding and seed sorting post-harvest. These modifications would need to be studied further to ensure they do not impact the vitality of the crops, for example, by testing whether pigments interfere with photosynthesis or plant resilience.
Future research will focus on improving remote sensing techniques to better detect these visually distinctive traits in crops and developing effective methods for training weeding robots to recognize and remove weeds while preserving the de novo crops. By combining genetic engineering with AI technologies, this innovative approach has the potential to revolutionize agriculture by creating visually distinctive, resilient, and high-yielding crops that are conducive to sustainable farming practices. Ultimately, this strategy aims to address the challenges of weed control in agriculture while promoting environmental sustainability and food security.
