Precision agriculture can foster cropland weed diversity and protect endangered species

Weed diversity plays an important role in the functioning of agroecosystems. Moreover, a number of endangered/threatened plant species occur as weeds in arable fields and/or field boundaries. Agricultural intensification has imposed negative consequences on weed diversity in general, and the survival of the endangered/threatened plant species in particular. The objective of this review is to provide a theoretical framework for promoting cropland weed diversity through precision agriculture. A systematic review was conducted based on literature analysis, existing knowledge gaps, and current needs to identify a suitable approach for promoting cropland biodiversity while protecting crop yields. While nonchemical weed management methods and economic threshold–based approaches are touted to improve weed diversity, they are either ineffective or insufficient for this purpose; long-term economic consequences and the risk of weed adaptation are major concerns. A plant functional trait-based approach to promoting weed diversity, one that considers a plant’s ecosystem service potential and competitiveness with the crop, among other factors, has been proposed by researchers. This approach has tremendous potential for weed diversity conservation in commercial production systems, but field implementation has been limited thus far due to our inability to selectively control weeds at the individual-plant level. However, recent advancements in computer vision, machine learning, and site-specific weed management technologies may allow for the accurate elimination of unwanted plants while retaining the important ones. Here, we present a novel framework for the utilization of precision agriculture for the conservation of cropland weed diversity, including the protection of endangered/threatened plant species, while protecting crop yields. This approach is the first of its kind in which the control priority is ranked on an individual-plant basis, by integrating intrinsic weed trait values with field infestation characteristics, while management thresholds are tailored to specific goals and priorities.