Location: Aerial Application Technology Research
2023 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
Work by this project in FY 2023 resulted in substantial progress towards improving aerial application methods and technologies used to apply agrochemical products in sustainable and effective ways. New commercially available and prototype drift reducing spray nozzle technologies were developed and evaluated for spray atomization performance using standard tank mixtures, with models being developed to provide aerial applicators with guidance on their proper selection and use (Objective 1). Evaluations were conducted to determine spray deposition characteristics for manned and unmanned aerial spray systems to establish effective techniques to enhance deposition uniformity and minimize environmental impact. Field trials were undertaken to generate spray drift curves for unmanned aerial spray systems under an assortment of meteorological and operational scenarios; these trials yielded significant data contributing to the advancement of risk assessment tools (Objective 1). Models were developed to understand the relationship between swath uniformity, effective width, and displacement as functions of droplet size, wind speed, and direction. The relationships established will serve to enhance the precision delivery of applied sprays, providing improved guidance to mitigate the risk of off-target movement, particularly during spray passes made at downwind field edges (Objective 1). A newly developed geospatial information system was coupled with aerial drone remote sensing to improve plant detection accuracy and efficiency in order to guide more effective and efficient control of crop pests (Objective 2). A comparative analysis between auto-exposure and fixed-exposure calibration methods for multispectral images was conducted, with the latter proving more effective in delivering significantly less radiometric error, and providing for improved confidence for decision-making based on such data (Objective 2).
Accomplishments
1. Reducing off-target damage from synthetic auxin herbicides. The global use of synthetic auxin herbicides is frequently linked with unintended damage to non-target crops and pollinators, disrupting effective weed management. ARS researchers at College Station, Texas, in cooperation with academic colleagues, conducted field trials to measure the downwind movement and damage of these herbicides. The work assessed the impacts of different application techniques and weather conditions on spray drift and deposition, with a specific focus on soybean impacts using florpyrauxifen-benzyl. The study revealed that aerial applications resulted in double the downwind deposition and plant damage compared to ground applications. However, offsetting aerial spray passes several swaths in the upwind direction significantly reduced both deposition and plant damage. These findings provide valuable guidance for better herbicide management practices, demonstrating that specific application adjustments can significantly reduce drift and associated crop damage. This research offers vital insights to ARS partners and stakeholders, and will facilitate mitigation of the negative impacts of auxin herbicides on non-target crops and pollinators, thus contributing to more sustainable agricultural practices and broader protection of the environment.
2. Optimizing UAV spray patterns for pest management. The field of agriculture is rapidly embracing the use of Unmanned Aerial Vehicles (UAVs) for pest management through application of agrochemical products. However, there is a significant gap in the data related to spray pattern uniformity and droplet distribution that is essential to guiding best management practices of these systems; understanding these factors is critical to their sustainable and effective use. ARS researchers at College Station, Texas, addressed this need by examining the effects of application height and ground speed on these parameters. Four commercially available UAV platforms, each configured with different payload capacities and factory-supplied nozzles, were tested. The results showed that each UAV platform could produce an acceptable spray pattern, irrespective of payload capacity, and that an increase in payload widened the effective swath. These findings provide valuable guidance for aerial applicators in using UAVs effectively in pest management. This accomplishment is significant to ARS partners and stakeholders in that it will facilitate development of efficient pest management strategies, to enhance the sustainable use of UAVs in agriculture.
Review Publications
Fritz, B.K., Sun, S., Kruger, G. 2022. Standardizing agricultural spray droplet size distributions. American Society for Testing and Materials. http://doi.org/10.1520/STP164120210077.
Guo, Z., Yang, C., Yang, W., Chen, G., Jiang, Z., Wang, B., Zhang, J. 2022. Panicle Ratio Network: Streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field. Journal of Experimental Botany. 71(19):6575-6588.
Yadav, P.K., Thomasson, J.A., Hardin, R., Searcy, S.W., Braga-Neto, U., Popescu, S.C., Martin, D.E., Rodriguez, R., Meza, K., Encisco, J., Diaz, J.S., Wang, T. 2022. Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery. Computers in Agriculture. https://doi.org/10.1016/j.compag.2022.107551.
Butts, T.R., Fritz, B.K., Kouame, B., Norsworthy, J.K., Barber, L.T., Ross, J., Lorenz, G.M., Thrash, B.C., Bateman, N.R., Adamczyk Jr, J.J. 2022. Herbicide spray drift from ground and aerial applications: Implications for potential pollinator foraging sources. Nature Plants. https://doi.org/10.1038/s41598-022-22916-4.
Zhao, H., Yang, Y., Yang, C., Song, R., Guo, W. 2023. Evaluation of spatial resolution on crop disease detection based on multiscale images and category variance ratio. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.107743.
Bonds, J., Fritz, B.K., Thistle, H. 2022. A field programme for the spray distribution of Unmanned Aerial Spray Systems (UASS) and the development of larvicide systems for vector control. Aspects of Applied Biology. 147:105-115.
Martin, D.E., Latheef, M.A. 2022. Payload capacities of remotely piloted aerial application systems affect spray pattern and effective swath. Drones. https://doi.org/10.3390/drones6080205.
Martin, D.E., Latheef, M.A., Duke, S.E. 2022. Gap optimization of electrostatic aerial spray nozzles for low-speed aircraft. Journal of Electrostatics. https://doi.org/10.1016/j.elstat.2022.103714.
Zhang, J., Sun, B., Yang, C., Wang, C., You, Y., Zhou, G., Liu, B., Wang, C., Kuai, J., Xie, J. 2022. A novel composite vegetation index including solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107031.
Li, Z., Angerer, J.P., Jaime, X., Yang, C., Wu, X. 2022. Estimating rangeland fine fuel biomass in western Texas using high-resolution imagery and machine learning. Remote Sensing. 14(17). Article 4360. https://doi.org/10.3390/rs14174360.
