The past 50 yr of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of nonselective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. Whereas previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

Colleges and universities around the world engaged diverse strategies during the COVID-19 pandemic. Baylor University, a community of ˜22,700 individuals, was 1 of the institutions which resumed and sustained operations. The key strategy was establishment of multidisciplinary teams to develop mitigation strategies and priority areas for action. This population-based team approach along with implementation of a “Swiss Cheese” risk mitigation model allowed small clusters to be rapidly addressed through testing, surveillance, tracing, isolation, and quarantine. These efforts were supported by health protocols including face coverings, social distancing, and compliance monitoring. As a result, activities were sustained from August 1 to December 8, 2020. There were 62,970 COVID-19 tests conducted with 1435 people testing positive for a positivity rate of 2.28%. A total of 1670 COVID-19 cases were identified with 235 self-reports. The mean number of tests per week was 3500 with approximately 80 of these positive (11/d). More than 60 student tracers were trained with over 120 personnel available to contact trace, at a ratio of 1 per 400 university members. The successes and lessons learned provide a framework and pathway for similar institutions to mitigate the ongoing impacts of COVID-19 and sustain operations during a global pandemic.

Resilience is a cross-disciplinary concept that is relevant for understanding the sustainability of the social and environmental conditions in which we live. Most research normatively focuses on building or strengthening resilience, despite growing recognition of the importance of breaking the resilience of, and thus transforming, unsustainable social-ecological systems. Undesirable resilience (cf. lock-ins, social-ecological traps), however, is not only less explored in the academic literature, but its understanding is also more fragmented across different disciplines. This disparity can inhibit collaboration among researchers exploring interdependent challenges in sustainability sciences. In this article, we propose that the term lock-in may contribute to a common understanding of undesirable resilience across scientific fields.

Field studies were conducted to evaluate the influence of herbicide application timing on weed control in no-till soybean production. Row spacing generally had no effect on weed control. Herbicide treatments containing chlorimuron plus metribuzin applied as many as 45 days prior to planting in 1988 and 1989 controlled broadleaf weeds throughout the growing season. Imazaquin applied 45 and 30 days prior to planting provided poor control of common cocklebur in 1989. Giant foxtail control was inconsistent with all herbicide treatments. Soybean yields subsequent to early preplant herbicide applications were greater than or equal to those in which applications were made at planting when late-season weed control was adequate. Herbicides applied preemergence did not control high densities of common lambsquarters in 1989.