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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Research Project #438007

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

Location: Aerial Application Technology Research

2020 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.

Progress Report
This is a new project which began April 13, 2020, replacing the expired bridging project, 3091-22000-036-00D, “Aerial Application Technology for Sustainable Crop Production.” Work under this project in fiscal year 2020 resulted in substantial progress towards improving aerial application of crop production and protection materials in an environmentally safe and effective manner. Standard spray solutions that span real-world tank mixture physical property ranges were identified for use in standardizing atomization research efforts used to guide proper selection and operational setup of application systems when using multiple pesticide chemistries and tank mix partners (Objective 1). New image analysis methods were developed to measure spray deposition coverage and droplet size metrics for artificial collectors used in spray application field trials. The new methods facilitated visualization of spray pattern uniformity with respect to applied rate and droplet size across multiple, overlapping spray passes (Objective 1). The new analysis method was used to compare spray deposition uniformity and drift from applications made with different boom configurations, showing potential drift mitigation with similar deposition uniformities at reduced boom widths (Objective 1). New imagery systems were developed for use on both manned and unmanned systems that provide for high quality, high resolution image acquisition on both platforms (Objective 2). Existing satellite image acquisition services were identified and compared to those developed for aerial platforms, demonstrating the need for improved analysis algorithms that can account for reduced image quality and resolution (Objective 2). Manned and unmanned image acquisition systems and analytic methods were developed 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.

1. Automatic classification of cotton root rot disease based on unmanned aerial vehicle (UAV) remote sensing. Cotton root rot is a persistent and destructive fungal disease of cotton in the southwestern and south central United States. Although cotton root rot can now be mitigated by Topguard Terra fungicide, the high treatment cost requires that it be applied only to infested areas. ARS researchers at College Station, Texas, working with Texas A&M University collaborators, developed two new automated classification methods that take advantage of the high-resolution imagery acquired using unmanned aerial vehicles. Comparative assessments of the methods showed that the new automatic methods provided for a higher degree of accuracy than conventional classification methods. The methodology and results from this study provide another useful tool to growers for site-specific management of cotton root rot as more and more growers are using UAVs for crop monitoring and disease detection.

2. Standardized spray solutions for atomization research. Spray droplet size, long recognized as a key factor driving the movement and fate of applied sprays, is influenced not only by nozzle selection and operation, but by the physical properties of the spray solution being applied. With the numerous pesticide chemistries and formulations available, coupled with the wide range of tank mix partners on the market, testing all combinations of tank mixtures and nozzle selections is not feasible. ARS researchers at College Station, Texas, working with academic and commercial research partners, developed a series of standard tank mixtures whose physical properties span those values typically observed in real-world field applications. These standard tank mixtures allow for harmonized spray atomization research across laboratories, providing for larger, pooled data sets. Utilization of these standard tank mixtures will facilitate greater understanding of the processes involved, ultimately delivering to applicators more reliable and consistent guidance on choosing nozzles to account for tank mix impacts and assure safe and efficacious applications.

Review Publications
Zhang, J., Wang, C., Yang, C., Jiang, Z., Zhou, G., Wang, B., Shi, Y., Zhang, D., You, L., Xie, J. 2020. Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: Performance and influencing factors. Precision Agriculture.
Yang, C. 2020. Airborne remote sensing systems for precision agriculture. Smart Agriculture. 2(1):1-22.
Wang, T., Thomasson, J.A., Yang, C., Isakeit, T., Nichols, R.L. 2020. Automatic classification of cotton root rot disease based on UAV remote sensing. Remote Sensing. 12, 1310.
Zhang, J., Xie, T., Yang, C., Song, H., Jiang, Z., Zhou, G., Zhang, D., Feng, H., Xie, J. 2020. Segmenting purple rapeseed leaves in the field from UAV RGB imagery using deep learning as an auxiliary means for nitrogen stress detection. Remote Sensing. 12, 1403.