Location: Aerial Application Technology Research2016 Annual Report
1a. Objectives (from AD-416):
Objective 1: Optimize aerial spray technologies for on-target deposition and drift mitigation. Subobjective 1A: Characterize effects of spray systems and formulations on droplet size. Subobjective 1B: Enhance spray swath deposition and uniformity. Subobjective 1C: Develop criteria for efficient operation and selection of aerial spray systems. Objective 2: Develop geospatial data processing and analyses methods for crop condition assessment and pest management. Subobjective 2A: Develop variable rate application methods for plant growth regulators (PGRs) and defoliants based on physiological conditions. Subobjective 2B: Develop precision aerial application methods for fertilization and disease control based on biotic conditions.
1b. Approach (from AD-416):
Utilizing engineering and biological principles, laboratory and field studies will be conducted to evaluate the effects of various aerial application parameters, such as spray formulation and droplet size, on aerial application efficiency and biological efficacy. Efforts will focus on the integration of laboratory spray droplet measurements and remote sensing systems to maximize the efficacy of crop production materials while minimizing any off-target impact from these sprays. Plant health and species differences will be determined from remotely sensed data and used to make spray application decisions related to spatial locations and dosages. This project will develop and implement new and improved aerial application technologies for safe, efficient, and sustainable crop production and protection.
3. Progress Report:
Work under this project during FY 2016 resulted in significant progress in improving the effective use of crop production and protection materials, enhancing the use of remote sensing and precision application in crop production systems, and spray droplet modeling with an emphasis on rotary-wing aircraft models. Significant progress was made in development of spray deposition and drift models, which will aid spray applicators in making spray applications that increase efficacy and minimize off-target spray drift. Wind-tunnel tests were completed to determine the levels of spray drift mitigation from a number of spray nozzles and formulations including real-world tank mixes used by aerial applicators. A variable-rate controller was installed and is currently under field usage for screwworm dispersion with the APHIS-Screwworm Barrier Maintenance Program in Panama. Free smartphone applications along with the Unit’s spray atomization models were further developed and modified for the iPhone platforms that transfer the project’s research data into more useful formats for our customers. Remote sensing studies were conducted that identified volunteer cotton plants in ditches and waterways, and diseased cotton plants. The data from these flights was coupled with prescription application maps to regulate the usage of agrochemicals based on field conditions. Numerous remote sensing flights were conducted to monitor the spread of cotton root rot at two locations in Texas. A dual-camera imaging system for remote sensing studies was developed using a consumer-grade camera and transferred to aerial applicators to use in their operations. These projects support the DoD-Deployed WarFighter Protection Program with specific collaborations with the U.S. Navy Entomology Center of Excellence, EPA Drift Reduction Technology (DRT) Program, several agrochemical companies, and equipment manufacturers. Project scientists during FY 2016 served on numerous occasions as experts in the aerial application industry and were sought out for advice and consultation by industry and academic research personnel, and by officials with the EPA, Department of Homeland Security, Department of Defense, State Department, USDA-APHIS, and representatives from numerous state agencies and organizations. This project supports National Programs 305, 304, and 104.
1. Applying droplet sizing calculators to optimize aerial applications. Successful aerial applications start with proper spray nozzle selection, setup and operation to ensure the resulting droplet size meets both pesticide product label requirements and meteorological and geographical conditions at the site of application. Recent computational droplet sizing models developed by ARS researchers at College Station, Texas, were adapted to a visual interface for use by applicators when making nozzle selections and operational decisions. Using the computational models, graphical representations of each nozzle's full operational spectrum were developed with droplet size classification data visually represented for quick and ready interpretation and selection. The new graphical interfaces greatly enhance the usability of the aerial nozzle droplet sizing models, thus allowing applicators to more quickly make operational decisions and document application parameters in order to meet pesticide product labels and ultimately optimize applications.
2. Consumer-grade cameras for aerial application. Consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. ARS researchers at College Station, Texas, assembled two imaging systems consisting of consumer-grade cameras for use on agricultural aircraft. They evaluated imagery acquired by the systems over a large cropping area for crop identification and assessment and compared different image mosaicking techniques for image stitching. Image classification and accuracy assessment showed that both normal color and near-infrared imagery acquired by the systems was useful for crop identification and crop growth assessment. The results from this study have provided useful information for aerial applicators and other remote sensing practitioners on the use of consumer-grade cameras. More than 20 aerial applicators have shown strong interest in assembling and using these cameras in their operations.
5. Significant Activities that Support Special Target Populations:
Fritz, B.K., Hoffmann, W.C., Anderson, J. 2016. Response surface method for evaluation of the performance of agricultural application spray nozzles. Pesticide Formulation and Delivery Systems: 35th Volume, American Society for Testing and Materials STP1587, G.R. Goss, ed., ASTM International, West Conshohocken, PA. pp. 61-76. doi:10.1520/STP158720140100.
Creech, C., Henry, R., Fritz, B.K., Kruger, G. 2015. Influence of herbicide active ingredient, nozzle type, orifice size, spray pressure, and carrier volume rate on spray droplet size characteristics. Weed Technology. 29(2):298-310.
Farooq, M., Hoffmann, W.C., Fritz, B.K., Cote, N., Walker, T., Smith, V. 2016. Evaluation of spray droplet spectrum of sprayers used for vector control. Atomization and Sprays. 26(8):739-754.
Toose, L., Warren, C., Mackay, D., Pinkerton, T., Letinski, D., Manning, R., Connelly, M., Rohde, A., Fritz, B.K., Hoffmann, W.C. 2015. Assessing the fate of an aromatic hydrocarbon fluid in agricultural spray applications using the three-stage ADVOCATE model framework. Journal of Agricultural and Food Chemistry. 63(31):6866-6875.
Song, H., Yang, C., Zhang, J., Hoffmann, W.C., He, D., Thomasson, A. 2016. Comparison of mosaicking techniques for airborne images from consumer-grade cameras. Journal of Applied Remote Sensing (JARS). 10:016030.
Hoffmann, W.C., Fritz, B.K., Yang, C. 2016. Effects of spray adjuvants on spray droplet size from a rotary atomizer. American Society for Testing and Materials. 35:52-60. doi:10.1520/STP1587201400992.
Yang, C., Sui, R., Lee, W. 2016. Precision agriculture in large-scale mechanized farming. In: Zhang, Q., ed., Precision Agriculture Technology for Crop Farming. CRC Press, Boca Raton, FL. p. 177-211.
Yang, C., Odvody, G., Thomasson, J., Isakeit, T., Nichols, R. 2016. Change detection of cotton root rot infection over a 10-year interval using airborne multispectral imagery. Computers and Electronics in Agriculture. 123:154-162.
Song, H., Yang, C., Zhang, J., He, D., Thomasson, J.A. 2015. Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot. Journal of Applied Remote Sensing (JARS). 9:096013.
Cribben, C.D., Thomasson, J.A., Ge, Y., Morgan, C.S., Yang, C., Isakeit, T., Nichols, R.L. 2016. Site-specific relationships between cotton root rot and soil properties. Journal of Cotton Science. 20:67-75.
Zhang, J., Yang, C., Song, H., Hoffmann, W.C., Zhang, D., Zhang, G. 2016. Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sensing. 8:257.