Precision Agriculture

, Volume 15, Issue 3, pp 290–303 | Cite as

Computational simulation of wireless sensor networks for pesticide drift control

  • Ivairton Monteiro Santos
  • Fausto Guzzo da Costa
  • Carlos Eduardo Cugnasca
  • Jó Ueyama
Article

Abstract

The efficient application of low cost pesticides is a challenge for agricultural production. Pesticide drift is a major cause of environmental contamination. At the time of application, it is essential to know the environmental conditions, such as wind, temperature and humidity to minimize contamination. This study proposes the use of wireless sensor networks in a support and control system for crop spraying and three cases of use are put forward. In the first case, the sensor network evaluates environmental data at the time of application to notify the user if the environmental conditions are suitable. The second use evaluates the wind speed and direction to suggest corrections in the path of a spray vehicle. Due to this alteration in the path, the pesticide is applied solely in the appropriate area. The final use involves collecting samples and analyzing the quality of crop spraying by evaluating the deposition of the pesticide over the crop. Through computer simulations, wireless sensor networks are shown to be useful in crop spraying operation to minimize and to control pesticide drift, to improve the quality of application, to reduce environmental contamination and to save time and money.

Keywords

Pesticide drift Crop spraying Wireless sensor network Simulation Embedded system 

References

  1. Adrian, A. M., Norwood, S. H., & Mask, P. L. (2005). Producers’ perceptions and attitudes toward precision agriculture technologies. Computers and Electronics in Agriculture, 48, 256–271. doi:10.1016/j.compag.2005.04.004.CrossRefGoogle Scholar
  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38, 393–422. doi:10.1016/S1389-1286(01)00302-4.CrossRefGoogle Scholar
  3. Bregaglio, S., Donatelli, M., Confalonieri, R., Acutis, M., & Orlandini, S. (2011). Multi metric evaluation of leaf wetness models for large-area application of plant disease models. Agricultural and Forest Meteorology, 151, 1163–1172. doi:10.1016/j.agrformet.2011.04.003.CrossRefGoogle Scholar
  4. Camilli, A., Cugnasca, C. E., Saraiva, A. M., Hirakawa, A. R., & Corrêa, P. L. P. (2007). From wireless sensors to field mapping: Anatomy of an application for precision agriculture. Computers and Electronics in Agriculture, 58, 25–36.CrossRefGoogle Scholar
  5. Craig, I. (2004). The GDS model—A rapid computational technique for the calculation of aircraft spray drift buffer distances. Computers and Electronics in Agriculture, 43, 235–250. doi:10.1016/j.compag.2004.02.001.CrossRefGoogle Scholar
  6. Dammer, K.-H., Thöle, H., Volk, T., & Hau, B. (2009). Variable-rate fungicide spraying in real time by combining a plant cover sensor and a decision support system. Precision Agriculture, 10, 431–442. doi:10.1007/s11119-008-9088-7.CrossRefGoogle Scholar
  7. Darr, M., & Hudson, K. (2004). Standardization of electronics in machinery systems: ISO 11783 nears completion for ag, construction, and forestry equipment. Engineering & Technology for a Sustainable World, 11, 13–14.Google Scholar
  8. Estrin, D., Girod, L., Pottie, G., Srivastava, M. (2001) Instrumenting the world with wireless sensor networks. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP’01) (Vol. 4, pp. 2033–2036).Google Scholar
  9. Faludi, R. (2010). Building wireless sensor networks. O’Reilly Media, Sebastopol. ISBN 978-0-596-80773-3.Google Scholar
  10. Giles, D. K., & Downey, D. (2003). Quality control verification and mapping for chemical application. Precision Agriculture, 4, 103–124. doi:10.1023/A:1021871207195.CrossRefGoogle Scholar
  11. Hewitt, A. (2000). Spray drift: Impact of requirements to protect the environment. Crop Protection, 19, 623–627. doi:10.1016/S0261-2194(00)00082-X.CrossRefGoogle Scholar
  12. Holterman, H., van de Zande, J., Porskamp, H. A., & Huijsmans, J. F. (1997). Modelling spray drift from boom sprayers. Computers and Electronics in Agriculture, 19, 1–22. doi:10.1016/S0168-1699(97)00018-5.CrossRefGoogle Scholar
  13. Kwong, K. H., Sasloglou, K., Goh, H. G., Wu, T. T. (2009) Adaptation of wireless sensor network for farming industries. In IEEE proceedings of sixth international conference on networked sensing systems (INSS) (pp. 1–4). Pittsburgh.Google Scholar
  14. Lan, Y., Thomson, S. J., Huang, Y., Hoffmann, W. C., & Zhang, H. (2010). Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and Electronics in Agriculture, 74, 34–38. doi:10.1016/j.compag.2010.07.001.CrossRefGoogle Scholar
  15. Larbi, P. A., & Salyani, M. (2012). CitrusSprayEx: An expert system for planning citrus spray applications. Computers and Electronics in Agriculture, 87, 85–93. doi:10.1016/j.compag.2012.05.005.CrossRefGoogle Scholar
  16. Lebeau, F., Verstraete, A., Stainier, C., & Destain, M. F. (2011). RTDrift: A real time model for estimating spray drift from ground applications. Computers and Electronics in Agriculture, 77, 161–174. doi:10.1016/j.compag.2011.04.009.CrossRefGoogle Scholar
  17. Lee, W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1, 95–113.CrossRefGoogle Scholar
  18. Oliveira, H. A. B. F., Barreto, R. S., Fontao, A. L., Loureiro, A. A. F. (2010) A Novel greedy forward algorithm for routing data toward a high speed sink in wireless sensor networks. In IEEE proceedings of 19th international conference on computer communications and networks (ICCCN) (pp. 1–7), Zurich.Google Scholar
  19. Ortiz, B. V., Thomson, S. J., Huang, Y., Reddy, K. N., & Ding, W. (2011). Determination of differences in crop injury from aerial application of glyphosate using vegetation indices. Computers and Electronics in Agriculture, 77, 204–213. doi:10.1016/j.compag.2011.05.004.CrossRefGoogle Scholar
  20. Pérez-Ruiz, M., Agüera, J., Gil, J. A., & Slaughter, D. C. (2011). Optimization of agrochemical application in olive groves based on positioning sensor. Precision Agriculture, 12, 564–575. doi:10.1007/s11119-010-9200-7.CrossRefGoogle Scholar
  21. Pottie, G. J., & Kaiser, W. J. (2000). Wireless integrated network sensors. Communications of ACM, 43, 51–58. doi:10.1145/332833.332838.CrossRefGoogle Scholar
  22. Qiao, X., Zhang, X., Wang, C., Ren, D., & He, X. (2005). Application of the wireless sensor networks in agriculture. Transactions of the Chinese Society of Agricultural Engineering, Z2, 232–234.Google Scholar
  23. Reyes, J. F., Correa, C., Esquivel, W., & Ortega, R. (2012). Development and field testing of a data acquisition system to assess the quality of spraying in fruit orchards. Computers and Electronics in Agriculture, 84, 62–67. doi:10.1016/j.compag.2012.02.018.CrossRefGoogle Scholar
  24. Varga, A. (2010). OMNeT++. Modeling and tools for network simulation. Aachen: Springer. ISBN: 978-3-642-12330-6.Google Scholar
  25. Vlajic, N., Xia, D. (2006) Wireless sensor networks: to cluster or not to cluster? In IEEE proceedings of international symposium on a world of wireless, mobile and multimedia networks. (WoWMoM 2006) (p. 268). Buffalo-Niagara Falls.Google Scholar
  26. Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—A worldwide overview. Computers and Electronics in Agriculture, 36, 113–132. doi:10.1016/S0168-1699(02)00096-0.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ivairton Monteiro Santos
    • 1
  • Fausto Guzzo da Costa
    • 2
  • Carlos Eduardo Cugnasca
    • 1
  • Jó Ueyama
    • 2
  1. 1.School of EngineeringUniversity of São PauloSão PauloBrazil
  2. 2.Institute of Mathematics and Computer ScienceUniversity of São PauloSão CarlosBrazil

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