Location: Aerial Application Technology Research
2022 Annual Report
Objectives
OBJECTIVE 1: Develop optimized aerial spray technologies for on-target deposition and drift mitigation for sustainable crop production.
Subobjective 1A: Develop decision support systems that support proper selection and use of spray technologies for improved product delivery and drift mitigation.
Subobjective 1B: Develop guidance for enhancing deposition uniformity across the effective swath width through proper setup of spray systems to account for the impacts of operational and meteorological conditions.
Subobjective 1C: Develop improved application methodologies to mitigate off-target movement and impact of applied sprays.
OBJECTIVE 2: Develop and/or evaluate remote sensing technologies for site-specific crop surveillance, assessment, and pest management across multiple imaging platforms and image processing techniques.
Research Goal 2A: Determine feasibility of using satellite and aerial imagery for early identification of cotton fields to support the boll weevil eradication program.
Subobjective 2B: Evaluate imagery from multiple platforms for effective detection and site-specific management of cotton root rot.
Subobjective 2C: Estimate cotton plant height using imagery from manned and unmanned aircraft for variable rate plant growth regulator application.
Approach
Aerial application is a critical component of American agriculture, accounting for almost 20% of all crop production and protection products applied on commercial farms in the U.S. and near 100% of those applied in forests. Given the scope of the industry, developing an understanding of the physical processes involved in driving the transport and ultimate fate of applied sprays is crucial. To this end, this project’s primary objectives center on developing spray technologies and methods that maximize targeted delivery of products while mitigating adverse impacts to non-target species and the environment and the development and use of remote sensing data to aid in the assessment of crop health and pest location to guide site-specific management of cropping systems. Through laboratory-based wind tunnel research, essential atomization characteristics of nozzles and spray formulations will be determined and incorporated into decision management systems that aid applicators in proper nozzle selection and operation. Field studies will then be used to optimize spray boom and nozzle positions on the boom that provide uniform coverage patterns under given application airspeeds and heights and in given meteorological conditions while minimizing the potential for off-target movement and damage to non-target species. Further, remote sensing data acquisition and analysis methods will be developed to determine site-specific crop and pest conditions and guide precision application of crop production inputs and pest management decisions.
Progress Report
Fiscal Year 2022 resulted in substantial progress towards improving aerial application methods and technologies used to apply agrochemical products in an environmentally safe and effective manner. New commercially available and prototype drift reducing nozzles were evaluated for spray atomization performance using a series of standard tank mixtures with new droplet size models developed and integrated in user interfaces that are routinely used by the industry (Objective 1). Manned and unmanned aerial spray systems were evaluated under standard, cross-wind field conditions. The work established the impact of wind speed and direction, and application settings, on spray deposition rate and droplet size characteristics across the effective swath with new techniques developed to provide greater deposition uniformity across the field (Objective 1). Spray swath displacement models were developed to provide greater precision in flight-line selection and delivery of applied sprays to intended target species and to prevent spray passes made at downwind field edges to contribute to off-target movement (Objective 1). A newly developed geospatial information system was coupled with aerial drone remote sensing to improve plant detection accuracy and efficiency to guide more effective and efficient control of crop pests (Objective 2). Improved models were developed using aerial drone remote sensing techniques to provide more accurate plant biophysical and canopy coverage data which will result in improved leaf area index estimations from remotely sensed data and guide precision crop management (Objective 2).
Accomplishments
1. Predicting volunteer cotton habitat for boll weevil eradication using GIS and remote sensing. The potential habitat for volunteer cotton in southern Texas creates the risk of encroachment of cotton boll weevils. ARS scientists at College Station, Texas, working with collaborators at Texas A&M University, developed a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the southern Texas cotton production regions, thus reducing the time and economic costs of their removal. GIS network analysis was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize the potential area of volunteer cotton growth. A method based on unmanned aerial vehicle (UAV) remote sensing was also proposed to detect the precise location of volunteer cotton plants in potential areas for subsequent removal. The proposed GIS network analysis model, coupled with UAV remote sensing, will provide boll weevil eradication program managers with an effective tool to identify potential habitat areas and precise locations of volunteer cotton.
2. Spray nozzles to reduce drift from aerial applications. Developing new technologies that improve the efficiency of aerial application while reducing spray drift is critical to the long-term sustainability of American agriculture. ARS scientists at College Station, Texas, identified and developed new nozzle technologies that significantly reduce the production of drift prone droplets and allow agricultural aircraft to operate at higher airspeeds for greater efficiency with reduced off-target movement. Wind tunnel studies were conducted to evaluate nozzle performance to facilitate improvements to nozzle design and reduce fine droplet development compared to traditional aerial application nozzle technologies. Droplet sizing models were developed to guide aerial applicators in proper nozzle selection and operation based on aircraft operational parameters, target pest species, and agrochemical product to ensure efficacy with minimal off-target impact. These droplet size models are widely used by the industry and provide an effective tool in helping applicators reduce spray drift potential while ensuring compliance with agrochemical product labels.
Review Publications
Faraji, A., Sorensen, B., Haas-Stapleton, E., Scholl, M., Goodman, B., Buettner, J., Schon, S., Ortiz, E., Hartle, J., Lefkow, N., Lewis, C., Fritz, B.K., Hoffmann, W., Williams, G. 2021. Utilization of unmanned aerial systems in mosquito and vector control programs. Journal of Economic Entomology. 114(5):1896-1909. https://doi.org/10.1093/jee/toab107.
Wang, T., Mei, X., Thomasson, A., Yang, C., Han, X., Yadav, P., Shi, Y. 2021. GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing. Computers and Electronics in Agriculture. 193. Article 106629. https://doi.org/10.1016/j.compag.2021.106629.
Vieira, B., Alves, G., Vukoja, B., Vinicius, V., Zaric, M., Houston, T., Fritz, B.K., Kruger, G. 2022. Spray drift potential of dicamba plus S-metolachlor formulations. Pest Management Science. https://doi.org/doi 10.1002/ps.6772.
Zhang, J., Biquan, Z., Yang, C., Yeyin, S., Qingxi, L., Guanscheng, Z., Chufeng, W., Tianjin, X., Zhao, J., Dongyan, Z., Wanneng, Y., Chenglong, H., Jing, X. 2020. Rapeseed stand count estimation at leaf development stages with UAV imagery and convolutional neural networks. Frontiers in Plant Science. 11:617. https://doi.org/10.3389/fpls.2020.00617.
Xie, T., Li, J., Yang, C., Jiang, Z., Chen, Y., Guo, L., Zhang, J. 2021. Crop height estimation based on UAV images: Methods, errors, and strategies. Computers and Electronics in Agriculture. 185:106155. https://doi.org/10.1016/j.compag.2021.106155.
Sun, B., Wang, C., Yang, C., Xu, B., Kuai, J., Li, X., Xu, S., Liu, B., Xie, T., Zhou, G., Zhang, J. 2021. Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter. International Journal of Applied Earth Observation and Geoinformation. 102:1-10. https://doi.org/10.1016/j.jag.2021.102373.
Jiang, Z., Tu, H., Bai, B., Yang, C., Zhao, B., Guo, Z., Liu, Q., Zhao, H., Yang, W., Xiaon, L., Zhang, J. 2021. Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. New Phytologist. 232(1):440-455. https://doi.org/10.1111/nph.17580.
