Project Number: 8042-22000-167-000-D
Project Type: In-House Appropriated
Start Date: Oct 1, 2020
End Date: Sep 30, 2025
Objective 1: Define theoretical, mechanistic, and applied underpinnings of weed management tactics in cover crop-based agronomic crop production systems to develop precision integrated weed management technologies. [NP304, Component 2, Problem Statement 2A&C] • Sub-objective 1.A. Determine optimal interactions between weed management tactics (physical, chemical, and biological) to address herbicide-resistant weeds and weed-crop competition. • Sub-objective 1.B. Develop cover crop and weed detection, identification, and mapping tools to assess the success of IWM systems and inform autonomous robotic weed control and decision support tools using machine vision technologies coupled with artificial intelligence (AI) and machine learning (ML).
To examine the interactions between cover crops and herbicides on weed-crop competition and weed suppression, we will quantify the interactive effect of cereal rye root and shoot residues with S-metolachlor rate on four weed species in the greenhouse. This will be conducted at Beltsville, Maryland and Urbana-Champaign. Next we will conduct field experiments that determine if cover crop management timing under field conditions influences the interactive effect between cereal rye and S-metolachlor, and whether the effect is due to physical (shoots) or chemical (roots via allelochemicals) mechanisms. This will be accomplished by establishing cover crop termination timing gradients and establishing a herbicide dose-response. The implications of management tactics will be further explored within a long-term cropping systems experiment. Here we will test the impact that harvest-time weed seed control, herbicides, and cover crops have on weed population dynamics and management genotypes in soybean by monitoring emergence, growth, survivorship, and fecundity of targeted summer annual weeds. To assess the performance of our IWM management systems at the field-scale, we will quantify the interaction between climate, soil, and cover crop management on cover crop performance and resulting weed suppression in an existing on-farm network across the U.S. Such an approach requires sensing technology to capture both the spatial distribution and performance. Therefore, we will develop digital weed image libraries from the on-farm network, annotate images, train machine learning models for image recognition, and test these algorithms with autonomous robots for weed control. Further, we will provide this information to growers through decision support tools.