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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Sustainable Agricultural Systems Laboratory » Research » Research Project #440796

Research Project: GROW: A National Response to the Herbicide-Resistant Weed Epidemic - VPI

Location: Sustainable Agricultural Systems Laboratory

Project Number: 8042-22000-167-031-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Aug 25, 2021
End Date: Aug 24, 2024

The herbicide-resistant weeds epidemic is limiting U.S. chemical weed control options and increasing crop yield losses. U.S. field crop producers need new precision technologies and nonchemical control practices to provide long-term herbicide resistant weed management. The overall goal of this project is to develop and regionally adapted precision Integrated Weed Management systems for corn, soybean, and cotton producers. Objective 1. Refine and regionalize Integrated Weed Management systems (Harvest Weed Seed Control, herbicides, and cover crops) to manage herbicide-resistant weed populations. Sub-objective 1.1. Obtain equipment and necessary technologies to conduct Harvest Weed Seed Control with farmer partners, develop image repositories, and hardware for weed density and biomass mapping. Sub-objective 1.2. On-farm assessments of how U.S. climate and cropping systems (corn, soybean, cotton, and wheat) interact with integrated weed management strategies (herbicides, Harvest Weed Seed Control, and cover crops) to affect weed seedbank dynamics, weed:crop competition, and weed control. Objective 2. Construct open-access technologies and a national library for weed and crop species identification, biomass and density estimation, supporting cyberinfrastructure, and user-friendly interfaces for precision Integrated Weed Management. Sub-objective 2.1. Construct an open image repository containing high resolution, annotated, images of common U.S. agronomic weeds in corn, soybean, cotton, and wheat (cotyledon to mature weeds) for species identification. Sub-objective 2.2. Develop a 3-dimensional reconstruction system (Weeds3D) for estimating crop and weed density and biomass using structure from motion analysis, stereo camera systems, and computer tablets. Sub-objective 2.3. Calibrate Weeds3D for agronomic crops (corn, soybean, cotton, and wheat) and common weeds; adapt the technology to work across hand-held, tractor-mounted, and UAV platforms for mapping weed and crop species, density, and biomass; and automate analysis and mapping of weeds with user-friendly interface. Sub-objective 2.4. Collaborate with equipment companies to evaluate our weed sensing solutions in combination with their precision technologies. Objective 3. Outreach, demonstration, and technology transfer. Sub-objective 3.1. Expand GROW’s (Getting Rid of Weeds) web-based outreach content including podcasts, webinars, and farmer case studies. Sub-objective 3.2. Debut decision support tools (DSTs) to inform growers about crop management in near- to real-time. Sub-objective 3.3. Engage farmers and agricultural professionals through interactive field days, equipment demonstrations, on-farm evaluations, and regional workshops and facilitate peer-to-peer knowledge transfer. Sub-objective 3.4. Market GROW and Integrated Weed Management beyond academia through educational and promotional booths at farmer- and consultant-focused workshops and conferences. Sub-objective 3.5. Publicly release all Artificial Intelligence applications and annotated image repositories.

The area-wide project is partitioned into three objectives. In objective one, the cooperator will participate in two field projects, an on-farm trial and an on-station trial. The on-farm experiment includes monitoring how cover crops and Harvest Weed Seed Control independently, and combined, suppress herbicide-resistant weeds. In the on-station trial, the cooperator will examine the trade-offs between planting into green cover crops versus brown cover crops on weed/herbicide interactions and water stress in soybeans. The cooperator will participate in the development of a weed image repository by using a high-resolution camera to take images of weeds in the field. In addition, the cooperator will provide calibration to a low-cost camera system that is used to estimate cover crop biomass. The calibration will require scanning weeds with a hand-held camera system and taking destructive biomass, density, and species samples. Finally, the cooperator will participate in extension activities by hosting field days, participating in webinars, providing expertise in determining weed parameters for the decision support tool, and farmer workshops.