Abstract
Current operator feedback from in-field pesticide application operations conveys limited information and often does not allow the operator to visualize a true representation of their performance. Farm management information systems (FMIS) typically do not account for overlap, varying application rates across the width of the spray boom during turns, or off-rate errors due to controller response. The pesticide application coverage training (PACT) tool was developed to deploy data analytics methodologies to sprayer operational data collected during field applications. The goal was to compare enhanced feedback via the PACT tool versus data generated from commercially available FMIS software today. Data were collected for multiple Nebraska fields and processed by the PACT program which consisted of a novel MATLAB program developed for this project. The PACT program successfully generated high-resolution as-applied maps and the automated application report further quantified the contributions of these errors to the total error to illustrate how overlap, turning errors or controller response issues may have individually affected application accuracy. PACT program output metrics were compared with current data provided by FMIS software. Field-average metrics were not found to be significantly different when comparing the PACT program to the FMIS output; however, when examining how in-field errors were distributed amongst various application rate ranges, significant differences were noted in comparison to the FMIS output. Thus, the PACT program was able to quantify and illustrate application rate variation due to boom section overlap and turning movements unaccounted for in traditional FMIS software.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
ASABE. (2016). S327.4: Terminology and definitions for applications of crop or forestry production and protective agents. St. Joseph, MI, USA: ASABE.
Darr, M. J. (2012). CAN bus technology enables advanced machinery management. Resource: Engineering & Technology for a Sustainable World, 19(5), 10–11.
Erickson, B., & LowenBerg-DeBoer, J. (2020). 2019 precision agricultural services dealership survey results. Dept. of Agricultural Economics/Dept. of Agronomy, Purdue University. Retrieved September 10, 2020, from https://ag.purdue.edu/digital-ag-resources/wp-content/uploads/2020/03/2019-CropLife-Purdue-Precision-Survey-Report-4-Mar-2020-1.pdf.
Giles, D. K., & Comino, J. A. (1990). Droplet size and spray pattern characteristics of an electronic flow controller for spray nozzles. Agriculture Engineering Research, 47(4), 249–267. https://doi.org/10.1016/0021-8634(90)80045-V.
Giles, D. K., & Downey, D. (2003). Quality control verification and mapping for chemical application. Precision Agriculture, 4, 103–124. https://doi.org/10.1023/A:1021871207195.
Kortenbruck, D., Griepentrog, H. W., & Paraforos, D. S. (2017). Machine operation profiles generated from ISO 11783 communication data. Computers and Electronics in Agriculture, 140, 227–236. https://doi.org/10.1016/j.compag.2017.05.039.
Kvaser. (2018). Kvaser Memorator Pro 2XHS V2. Retrieved September 10, 2020, fromhttps://canlandbucket.s3-website-eu-west-1.amazonaws.com/productionProductSheetFiles/989/73-30130-00821-2_EN_989.pdf.
Larson, J. A., Velandia, M. M., Buschermohle, M. J., & Westlund, S. M. (2016). Effect of field geometry on profitability of automatic section control for chemical application. Precision Agriculture, 17(1), 18–35. https://doi.org/10.1007/s11119-015-9404-y.
Luck, J. D., Pitla, S. K., Shearer, S. A., Mueller, T. G., Dillon, C. R., Fulton, J. P., et al. (2010a). Potential for pesticide and nutrient savings via map-based automatic boom section control of spray nozzles. Computers and Electronics in Agriculture, 70(1), 19–26. https://doi.org/10.1016/j.compag.2009.08.003.
Luck, J. D., Pitla, S. K., Zandonadi, R. S., Sama, M. P., & Shearer, S. A. (2011c). Estimating off-rate pesticide application errors resulting from agricultural sprayer turning movements. Precision Agriculture, 12(4), 534–545. https://doi.org/10.1007/s11119-010-9199-9.
Luck, J. D., Sharda, A., Pitla, S. K., Fulton, J. P., & Shearer, S. A. (2011b). A case study concerning the effects of controller response and turning movements on application rate uniformity with a self-propelled sprayer. Transactions of the ASABE, 54(2), 423–431. https://doi.org/10.13031/2013.36445.
Luck, J. D., Zandonadi, R. S., Luck, B. D., & Shearer, S. A. (2010b). Reducing pesticide over-application with map-based automatic boom section control on agricultural sprayers. Transactions of the ASABE, 53(3), 685–690. https://doi.org/10.13031/2013.30060.
Luck, J. D., Zandonadi, R. S., & Shearer, S. A. (2011a). A case study to evaluate field shape factors for estimating overlap errors with manual and automatic section control. Transactions of the ASABE, 54(4), 1237–1243. https://doi.org/10.13031/2013.39022.
Marx, S. E., Luck, J. D., Hoy, R. M., & Pitla, S. K. (2016). Comparing various hardware/software solutions and conversion methods for Controller Area Network (CAN) bus data collection. Computers and Electronics in Agriculture, 128, 141–148.
Marx, S. E., Luck, J. D., Hoy, R. M., Pitla, S. K., Darr, M. J., & Blankenship, E. (2015). Validation of machine CAN Bus J1939 fuel rate accuracy using Nebraska Tractor Test Laboratory fuel rate data. Computers and Electronics in Agriculture, 118, 179–185. https://doi.org/10.1016/j.compag.2015.08.032.
Neto, P. H. W., Meer, R. W. V. D., Vink, P., Justino, A., & Garcia, L. C. (2014). Automatic and manual spray bar sections control. Engenharia Agricola, 34(6), 1201–1209.
Pitla, S. K., Luck, J. D., Lin, N., Werner, J. P., & Shearer, S. A. (2016). In-field fuel use and load states of agricultural machinery. Computers and Electronics in Agriculture, 121, 290–300. https://doi.org/10.1016/j.compag.2015.12.023.
Pitla, S. K., Nin, L., Luck, J. D., & Shearer, S. A. (2015). Use of controller area network (CAN) data to determine field efficiencies of agricultural machinery. Applied Engineering in Agriculture, 30(6), 829–839. https://doi.org/10.13031/aea.30.10618.
Porter, W. M., Rascon, J. A., Shi, Y., Taylor, R. K., & Weckler, P. A. (2013). Laboratory evaluation of a turn compensation control system for a ground sprayer. Applied Engineering in Agriculture, 29(5), 655–662. https://doi.org/10.13031/aea.29.10075.
Reynaldo, E. F., & Molin, J. P. (2011). Proposed methodology for evaluation of automatic controller of boom sections and spray. Engenharia Agricola, 31(1), 111–120.
Sama, M. P., Luck, J. D., & Stombaugh, T. S. (2015). Scalable control architecture for variable-rate turn compensation. Applied Engineering in Agriculture, 31(3), 425–435. https://doi.org/10.13031/aea.31.10848.
Sharda, A., Luck, J. D., Fulton, J. P., McDonald, T. P., & Shearer, S. A. (2013). Field application uniformity and accuracy of two rate control systems with automatic section capabilities on agricultural sprayers. Precision Agriculture, 14(3), 307–322. https://doi.org/10.1007/s11119-012-9296-z.
Shearer, C.A. (2018). Development of a sprayer performance diagnostic tool using improved mapping and error quantification practices. M.S. thesis. Lincoln, NE, USA: University of Nebraska-Lincoln, Department of Biological Systems Engineering. Retrieved September 10, 2020, from https://digitalcommons.unl.edu/biosysengdiss/80/.
Stone, M. L., Benneweis, R. K., & van Bergeijk, J. (2008). Evolution of electronics for mobile agricultural equipment. Transactions of the ASABE., 51(2), 385–390. https://doi.org/10.13031/2013.24374.
Teejet. (2018). Teejet technologies catalog 51A-M. Retrieved September 10, 2020, from https://www.teejet.com/literature_pdfs/catalogs/C51A-M/cat51a_metric.pdf.
USDA. (2012). Census of Agriculture, United States, Summary and State Data. Retrieved September 10, 2020, from https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chapter_1_US/.
USDA. (2017). Farm income and wealth statistics. Retrieved September 10, 2020, fromhttps://data.ers.usda.gov/reports.aspx?ID=17834.
U.S. EPA. (2017). Pesticides industry sales and useage: 2008–2012 market estimates. Retrieved September 10, 2020, from https://www.epa.gov/sites/production/files/2017-01/documents/pesticides-industry-sales-usage-2016_0.pdf.
Zandonadi, R. S., Luck, J. D., Stombaugh, T. S., Sama, M. P., & Shearer, S. A. (2011). A computational tool for estimating off-target application areas in agricultural fields. Transactions of the ASABE, 54(1), 41–49. https://doi.org/10.13031/2013.36251.
Acknowledgements
This work is supported by the Critical Agriculture Research and Extension program, Grant No. 2015-67029-23517/Project Accession No. 1006193 from the USDA National Institute of Food and Agriculture. The authors would also like to acknowledge the financial support of the United States Department of Agriculture National Institute of Food and Agriculture, Hatch Project #1009760. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shearer, C.A., Luck, J.D., Evans, J.T. et al. Pesticide application coverage training (PACT) tool: development and evaluation of a sprayer performance diagnostic tool. Precision Agric 22, 852–872 (2021). https://doi.org/10.1007/s11119-020-09761-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-020-09761-z