Location: Aerial Application Technology Research2021 Annual Report
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.
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.
Fiscal Year 2021 resulted in substantial progress towards improving aerial application of crop production and protection materials in an environmentally safe and effective manner. Commercially available aerial application spray nozzles were evaluated for variation in spray characteristics amongst identical, new nozzles, and the data were used to update droplet size models used by the industry (Objective 1). Manned and unmanned aerial application systems were evaluated to determine the impact of meteorological and application conditions on swath width, swath displacement, and deposition uniformity. New evaluation methods were developed to account for the swath displacement due to ambient winds to determine required spray line offsets to reduce off-target movement. (Objective 1). The new methods were used to improve spray deposition uniformity and minimize drift while maximizing effective spray swath width by optimizing nozzle type, application height, and forward flight speed (Objective 1). Different image classification methods were compared for early identification of cotton and crop fields using imagery from manned aircraft and satellites (Landsat 7 and 8, and Sentinel 2A and 2B) and the best methods were identified (Objective 2). Sentinel-2 satellite images and advanced classification and modeling techniques were evaluated to create classification and prescription maps for site-specific fungicide application for the control of cotton root rot (Objective 2). Manned and unmanned image acquisition systems and analytic methods were refined to provide for effective detection of crops pests, estimates of plant height and vigor, and detection of early growth cotton to guide site-specific pest management (Objective 2).
1. Sentinel-2 imagery to identify cotton root rot. Accurate identification of cotton root rot-infested areas within fields is critical for cotton growers to effectively control the disease. ARS researchers at College Station, Texas, used no-cost Sentinel-2 satellite images to create classification and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected, and advanced classification and modeling techniques were used for image classification and prescription map creation. Accuracy assessment showed that the prescription maps derived from Sentinel-2 imagery were accurate and practical compared with those based on airborne imagery. These results confirmed that it is possible to identify cotton root rot and create prescription maps using no-cost Sentinel-2 imagery and appropriate imaging processing techniques. Sentinel-2 technology can be valuable in crop monitoring and development of precision agricultural techniques to enhance agricultural productivity and profitability.
2. Optimizing effective spray swath width for unmanned aerial applications. Understanding the impact that the spray system and surrounding meteorology have on the uniformity of deposition across the spray swath and the degree to which that swath is displaced relative to the flight path, is critical in optimizing the flight line spacing for uniform coverage at the targeted rate. Additionally, ensuring that deposition rates and droplet size across the area of application meet pesticide label requirements is critical to ensuring efficacy and minimizing non-target damage. ARS researchers at College Station, Texas, developed improved measurement and analytical methods to provide quantitative measures of spray rate and droplet size corresponding to changes in application height and flight line orientation relative to wind direction. This work developed novel data with respect to the spray deposition patterns and the relationships between swath displacement, effective width, applied droplet size, and ambient wind patterns. The new methods and developed datasets are already being used by aerial applicators, other researchers, and chemical manufacturers to guide ongoing unmanned spray application research efforts focused on optimizing system configurations for improved efficiency, efficacy, and environmental stewardship.
Martin, D.E., Latheef, M.A., Lopez, J., Duke, S.E. 2020. Aerial application methods for control of weed species in fallow farmlands in Texas. Agronomy Journal. 10:11.
Wang, T., Thomasson, A., Yang, C., Isakeit, T., Nichols, R.L., Collett, R.M., Han, X., Bagnall, C. 2020. Unmanned aerial vehicle remote sensing to delineate cotton root rot. Journal of Applied Remote Sensing (JARS). 14(3):034522-1. https://doi.org/10.1117/1.jrs.14.034522.
Wang, T., Thomasson, A., Isakeit, T., Yang, C. 2020. A plant-by-plant-level cotton root rot identification method based on UAV remote sensing. Remote Sensing. 12:2453. https://doi.org/10.3390/rs12152453.
Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G., Xie, J. 2020. Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring. Remote Sensing. 12:1207. https://doi.org/10.3390/rs12071207.
Li, X., Yang, C., Huang, W., Tang, J., Tian, Y., Zhang, Q. 2020. Identification of cotton root rot by multifeature selection from Sentinel-2 images using random forest. Remote Sensing. 12:3504. https://doi.org/10.3390/rs12213504.
Martin, D.E., Latheef, M.A. 2019. Aerial application methods control spider mites on corn in Kansas. Experimental and Applied Acarology. 77:571-582.
Martin, D.E., Singh, V., Latheef, M.A., Bagavathiannan, M.V. 2020. Spray deposition on weeds (Palmer amaranth and Morningglory) from a remotely piloted aerial application system and backpack sprayer. Drones. 4(3):59. https://doi.org/10.3390/drones4030059.
Yang, C. 2020. Remote sensing and precision agriculture technologies for crop disease detection and management with a practical application example. Engineering. 6:528-532. https://doi.org/10.1016/j.eng.2019.10.015.
Zhao, H., Yang, C., Guo, W., Zhang, L., Zhang, D. 2020. Automatic estimation of crop disease severity levels based on vegetation index normalization. Remote Sensing. 12:1930. https://doi.org/10.3390/rs12121930.
Jiang, H., Wang, S., Cao, X., Yang, C., Zhang, Z., Wang, X. 2019. A shadow-eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains. International Journal of Digital Earth. 12(9):1013-1029.
Fritz, B.K., Martin, D.E. 2020. Measurement and analysis methods for determination of effective swath width from unmanned aerial vehicles. In: Elsik, C.M., editor. Pesticide Formulation and Delivery Systems: 40th Volume, Formulation, Application and Adjuvant Innovation. West Conshohocken, PA: ASTM International. p. 62-85. https://doi.org/10.1520/STP162720190132.