Research Project: Improved Aerial Application Technologies for Precise and Effective Delivery of Crop Production Products

Location: Aerial Application Technology Research

2024 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 under Objective 1 in FY 2024 collected spray atomization data for several new spray nozzle and tank mix adjuvant configurations with the data being integrated into existing atomization models and associated user interfaces. An improved droplet sizing methodology using high-speed imagery was developed as part of Objective 1 research. New surrogate test materials that provide a safer alternative to active pesticide products while mimicking their atomization characteristics were developed as part of Objective 1 wind tunnel research. The swath pattern data collected under previous Objective 1 research efforts was compiled and used to validate a method for determining the displacement distance of the depositing spray material relative to the point of application. Field experiment data were analyzed to determine correlations between displacement distances and wind speed during time of application. Further research conducted under Objective 1 collected additional spray efficacy and drift data for new spray nozzles and system configurations, with existing databases being updated. Research conducted under Objective 2 developed improved machine learning classification tools to improve the identification of potential habitat areas of volunteer cotton from satellite imagery. Additional Objective 2 efforts compared two camera calibration methods for image data collected with an unmanned aerial vehicle (UAV) to reduce the reflectance error. Other research under Objective 2 evaluated three data parameters for mapping crop types to improve classification results using satellite data composited monthly over the growing season. An improved image analysis method was developed (Objective 2) that allows high quality reflectance data to be acquired using raw image data taken from consumer grade cameras.

Accomplishments
1. Early growth cotton detection to support boll weevil eradication. Early identification of cotton fields at risk of boll weevil infestation is crucial for effective control and eventual eradication of this pest. While remote sensing is widely used for crop identification, research on, and methods for early identification of cotton are lacking. This has resulted in continued reinfestations near the Lower Rio Grande Valley of Texas and Mexico, which prevents complete eradication. ARS researchers at College Station, Texas, developed new, advanced image processing and analysis methods that rapidly and accurately identify early growth cotton fields from satellite acquired imagery. The resulting methods will be used to help eradication program managers rapidly identify unreported or otherwise undetected cotton fields which will improve weevil monitoring, detection, and control efforts.

2. Drift reducing technology to reduce aerial application spray drift. Identifying new technologies and methods that improve aerial application efficiency while reducing the risk of spray drift is crucial to the long-term sustainability of American agriculture. ARS researchers at College Station, Texas, identified and evaluated an aircraft-mounted airflow modification system for its ability to improve aircraft flight stability/control to enhance spray delivery performance. A series of field studies demonstrated that this technology both improved spray deposition under the aircraft and reduced the amount deposited downwind. This technology will improve both aircraft flight characteristics and application precision. The accomplishment is significant given it will directly contribute to increased farming efficiency and in a more environmentally sensitive and sustainable manner.

Review Publications
Bonds, J.A., Thistle, H., Fritz, B.K., Reynolds, W., Kimbell, P. 2023. Uncrewed aerial spray systems for mosquito control: Efficacy studies for space sprays. Journal of the American Mosquito Control Association. 39(4):223-230. https://doi.org/10.2987/23-7140.
Martin, D.E., Latheef, M.A. 2024. The effect of vortex generators on spray deposition and drift from an agricultural aircraft. Agricultural Engineering Journal. 6:1683-1696. https://doi.org/10.3390/agriengineering6020097.
Martin, D.E., Rodriguez, R., Woller, D.A., Reuter, K.C., Black, L.R., Latheef, M.A., Taylor, M., Lopez Colon, K.M. 2022. Insecticidal management of rangeland grasshoppers using a remotely piloted aerial application system. Drones. https://doi.org/10.3390/drones6090239.
Chen, H., Fritz, B.K., Lan, Y., Sheng, W., Jingfu, Z. 2020. Overview of spray nozzles for plant protection from manned aircrafts: Present research and prospective. International Journal of Precision Agricultural Aviation (IJPAA). 3(2):1-12. https://doi.org/10.33440/j.ijpaa.20200302.76.
Chen, H., Fritz, B.K., Hoffmann, W.C., Lan, Y., Jingfu, Z. 2021. Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). International Journal of Precision Agricultural Aviation (IJPAA). 14(1):38-49. https://doi.org/10.25165/j.ijabe.20211401.5714.
Bonds, J., Fritz, B.K., Thistle, H. 2023. Calculation of swath width and swath displacement for uncrewed aerial spray systems. Applied Engineering in Agriculture. 66(3):523-532. https://doi.org/10.13031/ja.15400.
Yang, C., Suh, C.P. 2023. Applying machine learning classifiers to Sentinel-2 imagery for early identification of cotton fields to advance boll weevil eradication. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108268.
Bagnall, G.C., Thomasson, J.A., Yang, C., Wang, T., Han, X., Sima, C., Chang, A. 2023. Uncrewed aerial vehicle radiometric calibration: A comparison of auto exposure and fixed exposure images. Journal of Applied Remote Sensing (JARS). https://doi.org/10.1002/ppj2.20082.
Jaime, X., Angerer, J.P., Yang, C., Walker, J., Mata, J., Tolleson, D., Wu, X. 2023. Exploring effective detection and spatial pattern of Prickly Pear Cactus (Opuntia genus) from airborne imagery before and after prescribed fires in the Edwards Plateau. Remote Sensing. 15(16). Article 4033. https://doi.org/10.3390/rs15164033.
Song, X., Yang, G., Xu, X., Zhang, D., Yang, C., Feng, H. 2022. Winter wheat nitrogen estimation based on ground-level and UAV-mounted sensors. Sensors. https://doi.org/10.3390/s22020549.
Liu, X., Xie, S., Yang, J., Sun, L., Liu, L., Zhang, Q., Yang, C. 2023. Comparisons between temporal statistical metrics, time series stacks and phenological features derived from NASA Harmonized Landsat Sentinel-2 data for crop type mapping. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108015.