Location: Aerial Application Technology Research2019 Annual Report
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.
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.
This is a bridging project implemented on October 23, 2018, to replace the expired project 3091-22000-032-00D. Work under this project will continue through on or about January 31, 2020, while the research plan for the next five years undergoes peer panel Office of Scientific Quality Review evaluation/approval. Work during FY 2019 resulted in significant progress towards improving aerial application of crop production and protection materials in an environmentally safe and effective manner. Existing spray droplet sizing models were further enhanced to incorporate per nozzle flowrate data allowing applicators to quickly determine the operational setup and physical number of nozzles needed to meet spray application rate and droplet size requirements as specified by pesticide labels (Objective 1). Working with the U.S. Environmental Protection Agency (EPA) and agrochemical industry partners, new methodologies and metrics were developed to assess the potential drift reduction benefit when using technologies designed to improve on-target delivery of applied products (Objective 1). Using a combination of quantitative and qualitative spray deposition measurement methods, variation in the volume per area and droplet size of spray deposited across the spray swath under multiple wind conditions was evaluated and used to provide applicators improved guidance for more uniform applications across a given field for both manned and unmanned application systems (Objective 1). Improved image acquisition systems and analysis methods were developed for use by aerial applicators to obtain remotely sensed images and generate high quality maps for identifying crop pest issues and monitoring crop health conditions (Objective 2). Improved methodologies for using readily available, low-cost satellite imagery were developed to guide effective site-specific management of cotton root-rot and boll-weevil eradication through timely identification of volunteer, early growth cotton (Objective 2). Work under this project continues to support and provide data to collaborating partners, including the National Cotton Council, the EPA, the National Agricultural Aviation Association and other agrochemical and application technology manufacturers. The project also provides support to and content for numerous applicator educational and training resources.
1. Aerial spray droplet size classification standard. Aerial applications of crop production and protection products require the applicator to select and operate the most appropriate spray nozzle and other application technologies required to meet agrochemical label requirements and site-specific environmental and geographical conditions. Specific to this is meeting the label specified droplet size, typically given as a relative droplet size classification. ARS scientists at College Station, Texas, developed a set of standard spray nozzles and operational pressures that cover the wide range of potential spray sizes, and that establish relative droplet size classification scheme boundaries. This classification method was developed into an international standard that was incorporated into the fixed-wing and helicopter droplet size models previously developed by this group. This new method and associated standard allows aerial applicators to select and operate the most appropriate nozzles that meet pesticide label requirements, optimize efficiency, and mitigate non-target impacts, thus resulting in more environmentally-sensitive aerial applications.
2. High resolution satellite imagery for precision agriculture. Successful monitoring and management of crop health and pests to guide efficient production and protection inputs requires high quality and timely remotely sensed data. ARS scientists at College Station, Texas, developed methods for using high resolution satellite imagery which was used to create site-specific pest management maps for guiding precision application of pest and disease control inputs. These methods were shown to be particularly beneficial in the detection and monitoring of cotton root rot with prescription maps generated to guide variable rate input of applicable disease control products. The methodology and results from this work are immediately applicable across a wide variety of crop pest types allowing for improved efficiency through precision application of crop production and protection products, reduced pesticide inputs, and maintenance or even enhancement of overall pest management efficacy.
Fritz, B.K., Hoffmann, W.C., Martin, D.E. 2018. Mass balance and swath displacement evaluations from agricultural application field trials. Journal of ASTM International. 1610:11-23. https://doi.org/10.1520/STP161020170204.
Fritz, B.K., Hoffmann, W.C. 2018. Establishing reference nozzles for classification of aerial application spray technologies. International Journal of Precision Agricultural Aviation (IJPAA). 1(1):10-14. https://doi.org/10.33440/j.ijpaa.20180101.0003.
Hoffmann, W.C., Fritz, B.K. 2018. Using laser diffraction to measure agricultural sprays: Common sources of error when making measurements. International Journal of Precision Agricultural Aviation (IJPAA). 1(1):15-18. https://doi.org/10.33440/j.ijpaa.20180101.0005.
Wilde, S.C., Hoffmann, W.C., Fritz, B.K. 2018. Nonlinear derivation of spread factor due to viscous energy losses. Journal of ASTM International. 1610:53-60. https://doi.org/10.1520/STP161020170241.
Butts, T., Luck, J., Fritz, B.K., Hoffmann, W.C., Kruger, G. 2019. Evaluation of spray pattern uniformity using three unique analyses as impacted by nozzle, pressure, and pulse-width modulation duty cycle. Pest Management Science. 75(7):1875-1886. https://doi.org/10.1002/ps.5352.
Souza, D., Vieira, B., Fritz, B.K., Hoffmann, W.C., Peterson, J., Kruger, G., Meinke, L. 2019. Western corn rootworm pyrethroid resistance confirmed by aerial application simulations of commercial insecticides. Scientific Reports. https://doi.org/10.1038/s41598-019-43202-w.
Teske, M., Thistle, H., Fritz, B.K. 2019. Modeling aerially applied sprays: An update to AGDISP model development. Transactions of the ASABE. 62(2):343-354. https://doi.org/10.13031/trans.13129.
Butts, T., Butts, L., Luck, J., Fritz, B.K., Hoffmann, W.C., Kruger, G. 2018. Droplet size and nozzle tip pressure from a pulse width modulation sprayer. Biosystems Engineering. 178:52-69. https://doi.org/10.1016/j.biosystemseng.2018.11.004.
Rinkevich Jr, F.D., Margotta, J.W., Pohkrel, V., Ottea, J.A., Healy, K.B., Walker, T.W., Vaeth, R.H., Aldridge, R.L., Fritz, B.K., Danka, R.G., Rinderer, T.E., Hoffmann, W.C., Linthicum, K. 2017. Limited impacts of truck-based ultra-low volume applications of mosquito adulticides on mortality in honey bees (Apis mellifera). Bulletin of Entomological Research. 107(6):724-733. https://doi.org/10.1017/S0007485317000347.
Butts, T., Samples, C., Franca, L., Dodds, D., Reynolds, D., Adams, J., Zollinger, R., Howatt, K., Fritz, B.K., Hoffmann, W.C., Luck, J., Kruger, G. 2018. Spray droplet size and carrier volume effect on dicamba and glufosinate efficacy. Pest Management Science. 74(9):2020-2029. https://doi.org/10.1002/ps.4913.
Butts, T., Samples, C., Franca, L., Dodds, D., Reynolds, D., Adams, J., Zollinger, R., Howatt, K., Fritz, B.K., Hoffmann, W.C., Luck, J., Kruger, G. 2019. Droplet size impact on efficacy of a dicamba-plus-glyphosate mixture. Weed Technology. 33(1):66-74. https://doi.org/10.1017/wet.2018.118.
Butts, T., Samples, C., Franca, L., Dodds, D., Reynolds, D., Adams, J., Zollinger, R., Howatt, K., Fritz, B.K., Hoffmann, W.C., Luck, J., Kruger, G. 2019. Optimum droplet size using a pulse-width modulation sprayer for applications of 2,4-D choline plus glyphosate. Agronomy Journal. 111(1):1425-1432. https://doi.org/10.2134/agronj2018.07.0463.
Martin, D.E., Latheef, M.A., McCracken, A. 2018. Aerial application methods for increasing fungicide deposition on corn. International Journal of Agricultural and Biosystems Engineering. 3(4):92-102.
Yang, C. 2018. High resolution satellite imaging sensors for precision agriculture. Frontiers of Agricultural Science and Engineering. 5(4):393-405. https://doi.org/10.15302/j-fase-2018226.
Yang, C., Odvody, G., Thomasson, J., Isakeit, T., Minzenmayer, R., David, D., Nichols, R. 2018. Site-specific management of cotton root rot using airborne and high resolution satellite imagery and variable rate technology. Transactions of the ASABE. 61(3):849-858. https://doi.org/10.13031/trans.12563.
Zhao, X., Zhang, J., Yang, C., Song, H., Yeyin, S., Xingen, Z., Zhang, D., Zhang, G. 2018. Registration for optical multimodal remote sensing images based on FAST detection, window selection and histogram specification. Remote Sensing. 10(5):1-21. https://doi.org/10.3390/rs10050663.
Zhang, J., Wang, X., Yang, C., Jian, Z., He, D., Song, H. 2018. Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras. Computers and Electronics in Agriculture. 151:196-206. https://doi.org/10.1016/j.compag.2018.06.010.
Zhao, B., Zhang, J., Yang, C., Zhou, G., Ding, Y., Yeyin, S., Zhang, D., Xie, J., Liao, Q. 2018. Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery. Frontiers in Plant Science. 9:1362. https://doi.org/10.3389/fpls.2018.01362.
Wu, M., Peng, D., Qin, Y., Niu, Z., Yang, C., Li, W., Hao, P., Zhang, C. 2018. An index of non-sampling error in area frame sampling based on remote sensing data. PeerJ. 6:e5824. https://doi.org/10.7717/peerj.5824.