Recipient Organization
OHIO STATE UNIVERSITY
1680 MADISON AVENUE
WOOSTER,OH 44691
Performing Department
(N/A)
Non Technical Summary
Commodity crop production is threatened by the evolution of weed biotypes that are resistant to multiple herbicide modes of action. These multiple herbicide resistant (MHR) weeds have rendered many common herbicide chemistries ineffective, causing severe yield losses in commodity crops and leading farmers to increase the number and volume of herbicide products used in their weed management programs. Over the long-term, however, sole dependency on herbicides for weed management will only exacerbate the MHR epidemic. Integrated weed management (IWM) programs that incorporate non-chemical strategies alongside effective chemical control options can reduce selection pressure for MHR . Site-specific weed management programs that use precision technologies to target individual weeds with effective chemical and/or non-chemical controls can also reduce selective pressure by accounting for the inherent spatial heterogeneity of weed populations and simultaneously detecting and preventing escapes of MHR weeds. Developing effective precision IWM strategies that emphasize prevention through multiple complementary tactics is critical for reducing the rate of herbicide resistance evolution and spread.Measuring the success of an IWM program is not based on the efficacy of each individual tactic in a given growing season, but rather on the collective ability of multiple tactics to manage weed populations at levels that prevent interference with crop production over multiple years. Thus, for effective IWM to be developed and adopted, there is a need for ways to monitor weed populations with higher spatial and temporal resolutions than is provided by traditional methods such as manual scouting and visual weed control ratings.Recent advances in computer vision (CV) technology have enabled low-cost sensing systems for estimating weed biomass and density at the species level. Information from these systems can provide high spatial resolution maps of weed populations that can assist with evaluating both short- and long-term effects of weed management tactics individually and in combinations.Demographic information about weed populations (i.e., seedling emergence, mortality, fecundity) obtained from CV systems can also be combined with data on the typical variation in environmental conditions at a site to predict the long-term impacts of combining multiple weed management strategies, thereby reducing the resources needed to directly monitor these effects. Utilizing CV technology to monitor weed populations at the species level will accelerate the development and adoption of effective precision IWM strategies, thereby combatting the MHR epidemic and improving the overall sustainability of weed management.Getting Rid Of Weeds (GROW; www.growiwm.org) is a national research and extension network of weed scientists, agronomists, computer scientists, and agricultural engineers collaborating to develop precision IWM technologies. The GROW team has previously developed Weeds3D, a low-cost CV platform for weed mapping. Weeds3D consists of a GoPro Camera, an Android tablet for camera control, image upload, and displaying weed maps, and a cloud-computing based image processing pipeline. Weeds3D is capable of generating 3D point cloud reconstructions of weed and crop plants at the species level that allow for the estimation of individual plant biomass. While this technology has many potential applications for precision weed monitoring and management, it has yet to be used as a tool for data collection in applied IWM research.This project will test the Weeds3D system's ability to accurately estimate weed demographic data (i.e., weed density, biomass, and fecundity at the species level) in a multi-state field experiment assessing the integration of cover crops (CCs) and residual herbicides for MHR weed control. At each research site three cover crop management treatments (No cover/fallow, cereal rye terminated 2 weeks prior to soybean planting, and cereal rye terminated at or soon after soybean planting) and two herbicide treatments (POST-only, and PRE-residual followed by POST) will be utilized in a factorial randomized complete block design to create a gradient of IWM intensity. Cover crop performance, weed demographics, and soybean yield will be measured using both traditional sampling methods (i.e., quadrat samples of density, biomass, yield) and the Weeds3D system. Data from quadrat sampling will be used to validate the weed density and biomass estimates produced by Weeds3D, and both datasets will be analyzed to evaluate the efficacy of the IWM treatments on weed populations and soybean yield. Results from this experiment will be used to inform best practices for managing cover crops and integrating them with effective herbicide programs to manage MHR weeds and slow resistance development and spread.In addition to evaluating the impacts of a promising IWM strategy on weed population dynamics within a growing season, the proposed project will also develop digital infrastructure and statistical approaches for predicting the long-term impacts of IWM strategies by parameterizing predictive weed population models with demographic estimates generated by the Weeds3D system. In short, this will involve developing simulation models to predict how MHR weed populations respond to IWM tactics over longer periods of time, and integrating these models with the existing Weeds3D image processing software and database through a new application programming interface, also developed as part of the project effort. These combined efforts will result in an improved Weeds3D system with a seamless data processing and analysis pipeline that will allow researchers with minimal programming or modeling experience to quickly and easily collect videos of research trials, extract information about weed demographics at the species level, and predict the long-term impacts of IWM tactics being tested in the trials. By facilitating the data collection and analysis process and thereby reducing the time, labor, and other resources necessary for IWM research, these tools promise to greatly accelerate the development and testing of IWM strategies and thereby provide more options for the sustainable management of MHR weeds.
Animal Health Component
50%
Research Effort Categories
Basic
0%
Applied
50%
Developmental
50%
Goals / Objectives
Project Goal 1.Use computer vision (Weeds3D) and traditional field sampling methods to evaluate the in-season impact ofintegrating CCs and residual herbicide programs on weed suppression and cash crop performance across broad environmentalgradients.Objective 1a: Develop and revise field experiment protocols.Objective 1b: Coordinate field experiment management and data collection with collaborators in seven states (OH, DE, GA, IL, KY, NC, PA).Objective 1c: Collect and process video data with Weeds3D system to obtain weed density, biomass, and fecundityestimates 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 multipleherbicide 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 weedcommunities and herbicide resistance development under varying management scenarios.
Project Methods
Field Experiment (Project Goal 1). The Weeds3D system will be used to monitor weed populations in an IWM field experiment carried out at two field sites in each of seven states (OH, DE, GA, IL, KY, NC, PA) over two cash crop growing seasons, for a total of 28 site-years of data. The experiment will use a factorial design with three levels of CC management and two levels of herbicide inputs as treatments. The cash crop will be soybean at all sites in both years. CC management levels will be a bare fallow control, a standard termination timing two weeks prior to cash crop planting, and a planting green termination timing at or up to two days after cash crop planting. The bare fallow treatment will receive a burndown herbicide application at the same time as the standard CC termination timing. Herbicide treatments will be a POST-only herbicide program when weeds reach height-based management thresholds, and a program that includes a PRE-residual herbicide application at cash crop planting followed by the same POST program.This treatment design will produce a gradient of IWM intensity through the interaction of CC and herbicide treatments. It will also provide substantial environmental gradients due to differences in soil types and weather conditions across site years. Weed population response to management and environmental gradients will be measured through both traditional weed population sampling methods (i.e., seedling emergence counts, dry biomass samples, seed production measurements) and the Weeds3D system at times with biological and management relevance during the growing season: 1) at CC termination/burndown, 2) at soybean emergence, 3) three and six weeks after soybean emergence to capture emerging weed cohorts, 4) at soybean physiological maturity, and 5) at soybean harvest. These sampling timings will allow us to capture differences in weed seedling emergence, growth, mortality, and fecundity under varying management conditions that will be used to parameterize life-cycle based population models. CC biomass at termination, soybean stand count at growth stage V3, soybean biomass at growth stage R5, and soybean yield will also be measured to be used as explanatory variables in weed population models. Environmental data will be obtained from the NOAA Climate Data Online database (ncei.noaa.gov/cdo-web) and the NRCS Web Soil Survey Database (nrcs.usda.gov/wps/protal/nrcs/main/soils/survey) and verified by researchers at each site.Cereal rye (Secale cereale L.) wasplanted as a winter CC at all sites in Fall 2022 and 2023. Data collection began at CC terminationin Spring 2023 and will conclude with the second year of soybean harvest in Fall 2024. All sites are currently part of the GROWresearch network and have participated in prior research and development of the Weeds3D system. Thus, all sites already havethe necessary Weeds3D hardware, have experience using the Weeds3D system for data collection, and will receive anyWeeds3D software updates automatically from the GROW technical support team.Data Processing Pipeline for Modeling Weed Population Dynamics (Project Goal 2). Video collected with the Weeds3D system will be processed using existing algorithms for 3D point cloud reconstructions of weed and crop biomass using the structure-from-motion technique that accounts for canopy height and density. Weed density and biomass estimates will be generated by this image analysis pipeline and will be validated using empirical data from the field experiment. New empirical relationships for estimating weed fecundity will be integrated into the current image analysis pipeline using weed biomass data from the field experiment and seed production data from a previous GROW experiment (Lazaro et al. 2022).A new data flow pipeline for building predictive population simulation models from these estimates will be developed using the open-source R PROSPER package (von Redwitz and de Mol 2020). PROSPER was specifically developed for modeling MHR weed populations using demographic parameters and provides functions for introducing experimental data directly into simulation models. Models are built by defining an initial weed population structure (i.e., seedbank density and weed species biology, including herbicide resistance traits) and any number of selection functions (i.e., factors such as climate, competition, or weed management interventions) that determine the likelihood of an individual with a given set of traits proceeding to the next lifecycle or selection step. Initial weed population structure and selection functions for CC weed suppression and the effects of residual herbicides will be calculated based on estimates generated by the Weeds3D data collection over the course of each field season. The modeling data flow will be enabled by the development of an application programming interface (API) that will allow data to be pulled directly from the existing Weeds3D database into the PROSPER modeling environment. Additional model parameters and selection functions, such as the proportion of individual plants exhibiting HR traits in a population of a given species, and weed species life cycle characteristics such as seedbank mortality and annual germination rates will be obtained from site managers or published literature as needed. Population dynamics over multiple years will be simulated using initial population demographics and probabilities of individual weeds surviving and reproducing under a given management regime. Outputs of population models built with PROSPER will include year-to-year weed population growth or decline as influenced by weed demography and IWM practices, and the prevalence of herbicide resistance genes in the population if initialgene frequencies and susceptibility of resistant biotypes to management interventions are provided. All of the data flow infrastructure developed for this project will be hosted in a public Github repository, thereby making it easily accessible and customizable for other researchers and potential end users. Future plans for the Weeds3D system include developing a web application for researchers to easily access and analyze data generated by the system. The Weeds3D API and population modeling workflow developed for this project will eventually be integrated into this application, facilitating their use by other researchers.Evaluation.Success of the project will be evaluated based on the successful completion of data collection from the field experiment testing delayed CC termination and residual herbicide programs, the creation and validation of weed population models using demographic information generated by the Weeds3D computer vision system, and the deployment of an open access web application connecting the current Weeds3D image processing pipeline to a user-friendly interface for evaluating IWM research trials using predictive population models. Progress on each of these objectives and the underlying activities associated with each will be assessed periodically during monthly project team meetings between the PD and the collaborating researchers at each of the participating sites. Additional feedback on project outcomes will be obtained from GROW advisory board members when the PD presents updates at semi-annual GROW meetings. All feedback will be used to revise research objectives and experimental protocols as needed to ensure successful completion of the project.