Research Project: Precision Integrated Weed Management in Conventional and Organic Crop Production Systems

Location: Sustainable Agricultural Systems Laboratory

2023 Annual Report

Objectives
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).

Approach
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.

Progress Report
This report covers the third year of this project. Five of the seven milestones were fully met while two milestones were partially met due to restrictions on greenhouse usage during the pandemic. Over the past year, 15 peer-reviewed manuscripts were submitted, of these thirteen were accepted for publication. The remaining manuscripts are in review or have been revised and resubmitted. These manuscripts present results from projects on cover crop-based corn and soybean production, cover crop management for optimal weed and nitrogen management, and multi-tactic integrated weed management solutions that combine cover crop, herbicides, reduced-tillage, and harvest weed seed control management. Furthermore, an autonomous robotic platform for high throughput phenotyping and for building a National Agronomic Plant Image Repository was developed. Additionally, the ability to estimate cover crop performance with satellite imagery including use of hyperspectral sensors was improved. Published research from this team also included advancement in the use of computer vision and machine learning for monitoring crops and weed identification. Published research on 3-dimensional construction of weeds using Structure-for-Motion with a GoPro camera system and our ability to estimate cover crop biomass up to 8000 kg ha-1 is particularly noteworthy. Ongoing advancements have been made to our web-based cover crop nitrogen calculator (CC-NCALC), improving the estimates of plant available nitrogen. For Sub-objective 1A, field trials were initiated to quantify the potential for synergistic interactions between herbicides and cover crops on weed suppression; all samples were collected. The Lower Chesapeake Bay-Long-Term Agricultural Research multi-tactic weed management cropping systems experiment initiated with past Area-Wide funds has been maintained and is in its eighth year; all samples were collected. Data collection on weed populations and seedbank dynamics in two long-term cropping system experiments was completed and the data analyzed. For Sub-objective 1B, the on-farm monitoring of cover crop performance and subsequent impact on weed suppression is in its second year. Several hand-held and tractor-mounted sensing technologies were developed for monitoring cover crop performance and weed species identification and biomass estimation. The computer vision and machine learning efforts hold promise for replacing destructive sampling. The camera systems used for building a weed image repository as well as 3-dimensional reconstruction of plant biomass have been tested on a greenhouse semi-autonomous robotic system (BenchBot). The unit is being calibrated and cyberinfrastructure necessary for data acquisition and management is under development. This technology coupled with an OAK-D camera from Luxonis took third place in a North American Open Computer Vision competition. New area-wide funds were secured to expand the national Integrative Weed Management Team GROW (Getting Rid Of Weeds) for an additional six years. Outreach efforts have expanded through the website and decision tools. We have also initiated a national on-farm multi-tactic weed management project examining harvest weed seed control and cover crops.

Accomplishments

Review Publications
Menalled, U.D., Adieux, G., Cordeau, S., Smith, R.G., Mirsky, S.B., Ryan, M.R. 2022. Cereal rye mulch biomass and crop density affect weed suppression and community assembly in no-till planted soybean. Ecosphere. 13:6.Article number e4147. https://doi.org/10.1002/ecs2.4147.
Thapa, R., Cabrera, M., Reberg-Horton, C., Dann, C., Balkcom, K.S., Fleisher, D.H., Gaskin, J., Hitchcock, R., Poncet, A., Mirsky, S.B., Schomberg, H.H., Timlin, D.J. 2023. Modeling surface residue decomposition and N release using the Cover Crop Nitrogen Calculator (CC-NCALC) . Nutrient Cycling in Agroecosystems. https://doi.org/10.1007/s10705-022-10223-3.
Thieme, A., Hively, W.D., Gao, F.N., Jennewein, J.S., Mirsky, S.B., Soroka, A., Keppler, J., Bradley, D., Skakun, S., McCarty, G.W. 2023. Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination. Agronomy Journal. 15:442–458. https://doi.org/10.1002/agj2.21207.
Jennewein, J.S., Lamb, B.T., Hively, W., Thieme, A., Thapa, R., Goldsmith, A.S., Mirsky, S.B. 2022. Integration of satellite-based optical and synthetic aperture radar imagery to estimate winter cover crop performance in cereal grasses. Remote Sensing. https://doi.org/10.3390/rs14092077.
Sapkota, B., Popescu, S., Rajan, N., Leon, R., Reberg-Horton, C.S., Mirsky, S.B., Bagavathiannan, M. 2022. Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton. Scientific Reports. 12. Article number19580. https://doi.org/10.1038/s41598-022-23399-z.
Dobbs, A.M., Bagavathiannan, M.V., Ginn, D., Mirsky, S.B., Reberg-Horton, C.S., Leon, R.G. 2022. New directions in weed research and management using 3-D imaging. Weed Science. 70:641-647. https://doi.org/10.1017/wsc.2022.59.
Singh, M., Thapa, R., Kukal, M.S., Mirsky, S.B., Jhala, A.J. 2022. Effect of water stress on weed germination,growth characteristics, and seed production:a global meta-analysis. Weed Science. https://doi.org/10.1017/wsc.2022.59.
Gao, F.N., Jennewein, J.S., Hively, W.D., Soroka, A., Thieme, A., Bradley, D., Keppler, J., Mirsky, S.B., Akumaga, U. 2022. Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment. Science of Remote Sensing. 7. Article 100073. https://doi.org/10.1016/j.srs.2022.100073.
Mirsky, S.B., Davis, B.W., Poffenbarger, H., Cavigelli, M.A., Maul, J.E., Schomberg, H.H., Spargo, J.T., Thapa, R. 2023. Managing cover crop C:N ratio and subsurface-banded poultry litter rate for optimal corn yields . Agronomy Journal. https://doi.org/10.1002/agj2.21369.
Schomberg, H.H., Mirsky, S.B., White, K.E., Thompson, A.I., Bagley, G.A., Garst, G.D., Bybee-Finley, K.A. 2023. Interseeded cover crop mixtures influence soil water storage during the corn phase of corn-soybean-wheat no-till cropping systems. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2023.108167.
Kumar, V., Singh, V., Flessner, M., Reiter, M., Mirsky, S.B., Haymaker, J. 2023. Cover crop termination options and application of remote sensing for evaluating termination efficiency. PLOS ONE. 18(4). Article e0284529. https://doi.org/10.1371/journal.pone.0284529.
Liebert, J., Cherney, J.H., Ketterings, Q.M., Mirsky, S.B., Pelzer, C.J., Ryan, M.R. 2023. Winter cereal species, cultivar, and harvest timing affect trade-offs between forage quality and yield. Frontiers in Sustainable Food Systems. 7:1067506. https://doi.org/10.3389/fsufs.2023.1067506.