Progress 09/01/21 to 08/31/22
Target Audience:The key target audiences for this project include cover crop, soybean, corn, and cotton industry stakeholders and research commodity groups. Our findings will also be of interest to the broader agricultural scientists, ecologists, modelers, and precisionagricultural scientists who are focused on understanding the interaction between integrated weed management and. Our targetaudiences will be informed of the research updates through production of timely reports, bulletins and presentations in stakeholder meetings and scientific conferences. Changes/Problems:The Advisory Board has yet to be established, however, invitations are currently being distributed with the goal of the first meeting in late 2022 or early 2023. What opportunities for training and professional development has the project provided?
How have the results been disseminated to communities of interest?First year results were presented to an audience of academics and industry members at the Southern Weed Science Society annual meeting in early 2022. Preliminary results have also been disseminated through traditional extension presentations and field days, noted under accomplishments. What do you plan to do during the next reporting period to accomplish the goals? Harvest cash crops (obj. 1) Final statistical analyses of CC impact on weeds (Obj. 1) Incorporate modeling results into IWM tool (Obj. 2) Finalize extension materials (Obj. 3) Train-the-trainer events including at Northeastern Cover Crops Council meeting (Obj. 3) Promote extension materials (Obj. 3)
What was accomplished under these goals?
Objective 1. Year 2 data collection has been completed except for harvest (yield) data. Data has been being entered. Objective 2. Digital imagery has been collected and is being compiled for processing. The imagery will be processed for year 2 this winter. Objective 3. Extension outreach. Co-PI Flessner delivered 5 extension presentations reaching a total audience of 230 that included using cover crops for weed management, supported by findings to date from this project. Co-PI Mark VanGessel and Eugene Law delivered presentations at Delaware and Maryland Weed Management Field Days on June 29th that included this project with a total audience of about 60. Co-Pi Muthu Bagavathainnan presented findings at the Texas Plant Protection Association Meeting, and were discussed to growers in two county extension meetings and a turn-row meeting in Southeast Texas.
Conference Papers and Presentations
Abstract: Chu, S.A., G. Labiche, & Lazaro, L.M. 2022. Suppression of Weeds Via Cover Crops in Soybean. Proc. South. Weed Sci. Soc. 75:30.
Progress 09/01/20 to 08/31/21
Target Audience:The key target audiences for this project include cover crop, soybean, corn, and cotton industry stakeholders and research commodity groups. Our findings will also be of interest to the broader agricultural scientists, ecologists, modelers, and precision agricultural scientists who are focused on understanding the interaction between integrated weed management and. Our target audiences will be informed of the research updates through production of timely reports, bulletins and presentations in stakeholder meetings and scientific conferences. However, the project was just initated one month ago, so there is nothing to report. Changes/Problems:The methodology for objective 2 has been altered, but the end goal has not. The changes in methodology has resulted in changes to technology and ease of the user. What opportunities for training and professional development has the project provided?Undergraduate and graduate students have taken or are curently taking the necessary training for this project. How have the results been disseminated to communities of interest?
What do you plan to do during the next reporting period to accomplish the goals?Objective 1. Demonstrate the interaction between climate, soil, and CC management on CC performance and resulting weed suppression in an existing on-farm network across the mid-Atlantic and southern US and through expansion of the network into new states with weed-focused sites. Demonstrations on weed suppression by CCs will be conducted using on-farm and on-station trials in soybean, corn, and cotton fields that have strips with and without CCs. A minimum of two blocks (i.e. replications) will be established in on-farm trials (more if resources permit) and four in on-station trials. The cropping system will be chosen based on a preferred system in each region and will begin with a soybean phase rotating into the subsequent crop. CCs will be established in late fall. No-CC strips will be established when planting by not planting an area or killing CCs with herbicides two weeks after emergence. Management practices as well as intrinsic factors will be recorded and included in the overall data set. CC biomass will be sampled from each plot (from five random 1 m2 quadrats/plot) just prior to termination and will also be analyzed for C:N ratio. Weed emergence will be monitored by counting weed species density (three most prevalent species) in permanently established 1 m2 quadrats (five quadrats/plot); seedling emergence will be counted at biweekly intervals until crop canopy closure and a final assessment at harvest. At harvest, species-wise weed density, ground coverage, biomass, and seed production (three most common species) will be measured in the quadrats. Weed biomass will be quantified by clipping weeds at the soil surface and hand-separated to quantify individual species, and oven-dried and weighed. Weed seed will be separated and quantified. Objective 2. Assess the impact of spatial heterogeneity on weed suppressive potential of cover crops using image analysis-based predictive models. Image and ground truth data collection. Digital images will be collected using a low-cost overhead imaging system mounted with a high-resolution RGB camera, which will be compared with ground truth data collected at the same time. The size of the experimental unit will be 1 m2, which will be replicated 5 times within each plot. First, the influence of CC live biomass on the suppression of winter annual weeds will be assessed by capturing overhead images during cover crop growth period at monthly intervals. Further, starting at CC termination, pictures will be taken every two weeks until crop canopy closure. Ground truth measurements include visual counts of weed density, ground cover rating (%), and aboveground biomass assessment, which will be carried out immediately following image acquisition in every experimental unit at each observation timing. Image analysis-model training. Following image acquisition, two different methods will be employed to analyze the acquired images depending on the level of complexity of the species in the mixture, which is expected to vary across environments. The images will be pre-assessed for quality and pre-processing requirements. The approved images will be standardized to eliminate the variations resulting from illumination and resolution differences and stored in a web database for multi-user processing. Approximately 100 images will be acquired outside of the treatment units within each study site at different plant growth stages to create trainable synthetic images using the image synthesis procedure. Plant segments will then be randomly affixed to the soil texture at random density, generating composite images and ground truth annotations. Different artificial neural network structures will be tested for semantic segmentation for best performance. In addition to synthesized images, true annotated images will be added to the training dataset to improve segmentation accuracy. For this purpose, approximately 1000 images will be collected outside of the treatment unit encompassing different plant growth stages and conditions within each experimental field. The trained model will then be used to classify pixels in the image under consideration for further assessment. It is expected that the model will be robust across multiple environments. Image analysis- model validation. The images acquired from within the experimental units (i.e. 1 m2 quadrats within each plot), will be used to validate the trained model in comparison with the ground truth data collected alongside. Both the object-based method and the neural network-based method developed on the training set will be validated on the experimental dataset. The validation process will also serve to assess the impact of cover crops on weed population dynamics. Assessment of weed suppression potential. Weed detection and classification results from image analysis will be further post-processed to generate information for weed density, canopy cover, and biomass. The canopy masks per species will be assessed against biomass to develop quantitative models that could be used to estimate biomass based on image-derived information, as biomass and canopy pixel coverage areas are shown to have non-linear relationships. Objective 3. Conduct IWM extension outreach and training as part of the implementation of weeds research in an existing on-farm network across the mid-Atlantic and southern US. On-farm research will serve as demonstration to other growers in a region and an opportunity for collaborative co-learning with researchers. The in- field sensor technologies employed by the existing on-farm research project, which allow farmers to view real-time field data (e.g., soil moisture in +/- CC plots), provide a novel means of engaging farmers as collaborators rather than passive recipients of information. Farmers participating in the proposed weeds research module will observe the impact of CCs and CC management decisions on weed emergence and growth and cash crop yields. Farmers will link observations with hard data, improve their decision-making based on these observations.
What was accomplished under these goals?
Objective 1. Year 1 data collection has been completed and data is being entered.Field locations for the on-farm and on-station trials have been initiated for year 2. Some locations have planted the cover crops. Objective2. Digital imagery has been collected and the methodology has been updated to reflect changes in technologies. The imagery will be processed for year 1 this winter. Objective 3. Extension outreach will begin this winter.