Precision Agriculture

, Volume 20, Issue 4, pp 840–856 | Cite as

Use of real-time extend GNSS for planting and inverting peanuts

  • Adão Felipe dos SantosEmail author
  • Rouverson Pereira da Silva
  • Cristiano Zerbato
  • Patricia Candida de Menezes
  • Elizabeth Haruna Kazama
  • Carla Segato Strini Paixão
  • Murilo Aparecido Voltarelli


Among the main techniques employed in precision agriculture, yield mapping and automatic guidance of agricultural machines are the best-known to farmers. The objective of this study was to evaluate, using statistical process control tools, the quality of automatic guidance using satellite signals, to reduce positioning errors and losses in peanut digging. The treatments consisted of the use of manual (operator guidance) and automatic (autopilot) guidance with RTX satellite signals in sowing and digging operations. The quality of the operation was evaluated after collection of 30 points spaced at 100 m for each quality indicator, which are the losses and the errors of alignment of the mechanised sets in sowing and digging operations. From the perspective of statistical control, manual guidance was shown to be compromised for the quality indicators of digging losses. Despite the instability in the sowing and digging operations, the use of automatic guidance proved to be accurate. The use of automatic guidance increases the precision and reduces overlaps (< 38 mm, as stipulated by the supplier) for sowing and digging. The manual sowing mean error between overlaps was stable; however, it did not remain constant over time.


Global navigation satellite system (GNSS) Precision point positioning Mechanized harvest Peanut digging 



This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Supplementary material

11119_2018_9616_MOESM1_ESM.pdf (84 kb)
Electronic supplementary material 1 (PDF 84 kb)


  1. Almeida, M. S., & Dal Poz, W. R. (2016). Precise point positioning and relative positioning with GNSS: What most accurate currently? Bulletin of Geodetic Science Curitiba, 22(1), 175–195.Google Scholar
  2. Baio, F. R. R., & Moratelli, R. F. (2011). Auto guidance accuracy evaluation and contrast of the operational field capacity on the mechanized plantation system of sugar cane. Journal of the Brazilian Association of Agricultural Engineering, 31(2), 367–375.Google Scholar
  3. Bragachini, M. E., & Peiretti, J. M. (2008). En la eficiencia de cosecha de maní (On the efficiency of the peanut harvest). Gacetilla de Prensa: 09/2008 março 2008. Retrieved September 3, 2018, from (in Spanish).
  4. Cappellli, N. L., Umezu, C. K., Silveira, A. C., & Garcia, A. P. (2006). Comparative performance among GPS receivers. Journal of the Brazilian Society of Agroinformatics, 8(1), 63–77.Google Scholar
  5. Cavichioli, F. A., Zerbato, C., Bertonha, R. S., Silva, R. P., & Silva, V. F. A. (2014). Pods losses during the mechanical harvesting of peanuts. Journal of Agrarian Science, 42(3), 211–215.Google Scholar
  6. CONAB. (2017). Monitoring of the Brazilian grain harvest, v.4 Harvest 2016/17-12° data collection, Brasília, pp. 1–158 September 2017. Retrieved September 3, 2018, from (in Portuguese).
  7. Easterly, D. R., Adamchuk, V. I., Kocher, M. F., & Hoy, R. M. (2010). Using a vision sensor system for performance testing of satellite-based tractor auto-guidance. Computers and Electronics in Agriculture, 72(2), 107–118.CrossRefGoogle Scholar
  8. EMBRAPA - Brazilian Agricultural Research Corporation. (2013). Brazilian system of soil classification (3rd ed., p. 353). Brasília: Embrapa.Google Scholar
  9. Holpp, M., Kroulik, M., Kviz, Z., Anken, T., Sauter, M., & Hensel, O. (2013). Large-scale field evaluation of driving performance and ergonomic effects of satellite-based guidance systems. Biosystems Engineering, 116(2), 190–197. Scholar
  10. Ince, A. & Guzel, E. (2003). Effects of gynophore breaking resistance on losses in mechanized peanut harvesting. In G. Quick (Ed.) International conference on crop harvesting and processing, ASAE Publication Number 701P1103e. St Joseph, MI, USA: ASAE.Google Scholar
  11. Keller, J. (2005). Auto-Guidance-System-Effiziente Flächenbearbeitung, Diesel verbrauchsoptimierung, Steigerung der Wirtschaftlichkeit (Efficient surface treatment, diesel consumption optimization, increase in economic efficiency). In: Max-Eyth-Gesellschaft Fur Agrartechnik (Ed.) VDI-Berichte 1868-Landtechnik für Profis (pp. 75–80). Düsseldorf, Germany: VDI Verlag GmbH.Google Scholar
  12. Koppen, W. (1948). Climatologia: Con um estudo de los climas de la Tierra. (Climatology: With a study of the climates of the Earth) México: Fondo de Cultura Economico.Google Scholar
  13. Kroulík, M., Kvíz, Z., Kumhála, F., Hula, J., & Loch, T. (2011). Procedures of soil farming allowing reduction of compaction. Precision Agriculture, 12(3), 317–333.CrossRefGoogle Scholar
  14. Leidner, J. (2012). Precision farming payoff in peanuts. Southeastern Peanut Farmer, 50(5), 10–12.Google Scholar
  15. Lipinski, A. J., Markowski, P., Lipinski, S., & Pyra, Paweł. (2016). Precision of tractor operations with soil cultivation implements using manual and automatic steering modes. Biosystems Engineering, 14, 22–28.CrossRefGoogle Scholar
  16. Montgomery, D. C. (2009). Design and analysis of experiments (6th ed., pp. 179–268). Hoboken, NJ, USA: Wiley.Google Scholar
  17. Oksanen, T. (2015). Accuracy and performance experiences of four wheel steered autonomous agricultural tractor in sowing operation. In L. Mejias, P. Corke, & J. Roberts (Eds.), Field and service robotics. Springer tracts in advanced robotics (Vol. 105). Switzerland: Springer. Scholar
  18. Oliveira, T. C., & Molin, J. P. (2011). Use of autopilots on citrus orchards establishment. Journal of the Brazilian Association of Agricultural Engineering, 31(2), 334–342.Google Scholar
  19. Ortiz, B. V., Balkcom, K. B., Duzy, L., Van Santen, E., & Hartzog, D. L. (2013). Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations. Precision Agriculture, 14, 357–375.CrossRefGoogle Scholar
  20. Paixão, C. S. S., Santos, A. F., Voltarelli, M. A., Silva, R. P., & Carneiro, F. M. (2017). Times of efficiency and quality of soybean crop mechanical operation in geometry functions of plots. Journal of the Brazilian Association of Agricultural Engineering, 37(1), 106–115. Scholar
  21. Perez-Ruiz, M., & Upadhyaya, S. K. (2012). GNSS in precision agricultural operations. In F. B. Elbahhar & A. Rivenq (Eds.), New approach of indoor and outdoor localization systems (pp. 1–24). InTech: London, United Kingdom.Google Scholar
  22. Rizos, C., Janssen, V., Robert, C., & Grinter, T. (2012). PPP versus DGNSS. Geomatic. World, 20, 18–20.Google Scholar
  23. Roberson, G. T. (2009). Planting, harvesting, and curing peanuts. In: D. L. Jordan, R. L. Brandenburg, A. B. Brown, S. G. Bullen, G. T. Roberson, B. Shew & J. F. Spears (Eds.) Peanut information (pp. 131–148). North Carolina Coop. Ext. Series AG-331. Raleigh, NC, USA.Google Scholar
  24. Roberson, G. T., & Jordan, D. L. (2014). RTK-GPS and automatic steering for peanut digging. Applied Engineering in Agriculture, 30(3), 405–409.Google Scholar
  25. Rocha, R. S. M., Jerez, G. O., Brassarote, G. O. N., & Monico, J. F. G. (2017). Assessment of the ionospheric scintillation and different data time intervals effect on the precise point positioning in its on-line versions. Revista Brasileira de Geomática, 5(2), 251–276.CrossRefGoogle Scholar
  26. Samohyl, R. W. (2009) Statistical quality control (1st ed.). São Paulo, Brazil: Elsevier: Elsevier Campus. v. 1. ISBN 978-85-352-3220-2.Google Scholar
  27. Santos, A. F., Kazama, E. H., Ormond, A. T. S., Tavares, T. O., & Silva, R. P. (2016a). Quality of mechanized peanut digging in function of the auto guidance. African Journal of Agricultural Research, 11(48), 4894–4901.CrossRefGoogle Scholar
  28. Santos, A. F., Silva, R. P., Corrêa, L. N., Borba, M. A. P., & Girio, L. A. S. (2016b) Accuracy and precision monitoring through statistical process control. In: Brazilian Congress of Precision Agriculture-ConBAP, 2016, Goiânia. Anais Goiânia: SBEA, CD-Rom.Google Scholar
  29. Santos, E. P., Silva, R. P., Bertonha, R. S., Noronha, R. H. F., & Zerbato, C. (2013). Productivity and losses in the peanut on five different harvesting dates. Revista Ciência Agronômica, 44(4), 695–702.CrossRefGoogle Scholar
  30. Seeber, G. (2003). Satellite geodesy: Foundations, methods and applications (p. 586). Berlin, New York: Walter de Gruyter.CrossRefGoogle Scholar
  31. Silva, E. L., & Gervásio, E. S. (1999). The use of TDR for moisture determination in different layers of a dystrophic dusky red latossol. Revista Brasileira Engenharia Agrícola Ambiental, 3(3), 417–420.CrossRefGoogle Scholar
  32. Tavares, T. O., Damasceno, A. F., Voltarelli, M. A., Silva, R. P., & Furlani, C. E. A. (2017). Effective power and hourly fuel consumption demanded by set tractor-coffee harvester in function of adequacy tractor ballasting. Journal of the Brazilian Association of Agricultural Engineering, 37(4), 699–708. Scholar
  33. Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: A review for policy implications. Precision Agriculture, 13, 713–730. Scholar
  34. Tiwari, R., Skone, S., Tiwari, S., & Strangeways H. J. (2011). 3WBMod assisted PLL GPS software receiver for mitigating scintillation affect in high latitude region. IEEE. Retrieved September 26, 2018 from,
  35. TRIMBLE RTX. (2017). Retrieved September 03, 2018 from
  36. USDA. (2018). United States Department of Agriculture. Peanut Area, Yield, and Production. Retrieved June 10, 2018 from
  37. Vaccaro, G. L. R., Martins, J. C., & Menezes, T. M. (2011). Statistical analysis of the quality of tension levels in electrical energy distribution systems. Production, 21, 539–552. Scholar
  38. Vellidis, G., Ortiz, B., Beasley, J., Hill, R., Henry, H., & Brannen, H. (2014). Reducing digging losses by using automated steering to plant and invert peanuts. Agronomy, 4, 337–348. Scholar
  39. Voltarelli, M. A., Silva, R. P., Cassia, M. T., Daloia, J. G. M., & Paixão, C. S. S. (2017). Quality of base cutting in sugarcane using knives of different angles and coatings. Revista Ciência Agronômica, 48(3), 438–447.CrossRefGoogle Scholar
  40. Williams, E. J., & Drexler, J. S. (1981). A non-destructive method for determining peanut pod maturity. Peanut Science, 8(2), 134–141.CrossRefGoogle Scholar
  41. Zerbato, C., Furlani, C. E. A., Ormond, A. T. S., Gírio, L. A. S., Carneiro, F. M., & Silva, R. P. (2017a). Statistical process control applied to mechanized peanut sowing as a function of soil texture. PLoS ONE, 12(7), e0180399. Scholar
  42. Zerbato, C., Furlani, C. E. A., Silva, R. P., Voltarelli, M. A., & Santos, A. F. (2017b). Statistical control of processes applied for peanut mechanical digging in soil textural classes. Journal of the Brazilian Association of Agricultural Engineering, 37(2), 315–322.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Adão Felipe dos Santos
    • 1
    Email author
  • Rouverson Pereira da Silva
    • 1
  • Cristiano Zerbato
    • 1
  • Patricia Candida de Menezes
    • 1
  • Elizabeth Haruna Kazama
    • 1
  • Carla Segato Strini Paixão
    • 1
  • Murilo Aparecido Voltarelli
    • 2
  1. 1.Department of Agricultural EngineeringSão Paulo State UniversityJaboticabalBrazil
  2. 2.Federal University of São Carlos, Lagoa do Sino CampusBuriBrazil

Personalised recommendations