ADVANCING INTEGRATED WEED MANAGEMENT RESEARCH WITH COMPUTER VISION TECHNOLOGY

Project Goal 1. Use computer vision (Weeds3D) and traditional field sampling methods to evaluate the in-season impact of integrating CCs and residual herbicide programs on weed suppression and cash crop performance across broad environmental gradients.Objective 1a: Develop and revise field experiment protocols.Objective 1b: Coordinatefield experiment management and data collectionwith collaborators in seven states (DE, GA, IL, KY,MD, NC, PA).Objective 1c: Collect and process video data with Weeds3D system to obtain weed density, biomass, and fecundity estimates for each species present in experimental transects.Objective 1d: Collect weed density, biomass, and fecundity data using standard quadrat sampling methods.Objective 1e: Validate weed demographic data estimates from Weeds3D using data from quadrat samples.Project Goal 2. Predict long-term effects of CC management and residual herbicides on weed communities, including multiple herbicide resistant weeds, using population models created from CV data.Objective 2a: Develop Weeds3D database API to enable seamless data flow between collection and analysis.Objective 2b: Develop weed population simulation models from Weeds3D demographic data.Objective 2c: Use population model results to evaluate long-term effects of cover crops and residual herbicides on weed communities and herbicide resistance development under varying management scenarios.