Skip to main content

ROBOFERT: Human - Robot Advanced Interface for Robotic Fertilization Process

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 407)

Abstract

The interfaces for Human-Robot interaction in different fields such as precision agriculture (PA) have made it possible to improve production processes, applying specialized treatments that require a high degree of precision at the plant level. The current fertilization processes are generalized for vast cultivation areas without considering each plant’s specific needs, generating collateral effects on the environment. The Sureveg Core Organic COfound ERA-Net project seeks to evaluate the benefits of growing vegetables in rows through the support of robotic systems. A robotic platform equipped with sensory, actuation, and communication systems and a robotic arm have been implemented to develop this proof of concept. The proposed method focuses on the development of a human-machine interface (IHM) that allows the integration of information coming from different systems from the robotized platform on the field and suggest to an operator (in a remote station) take a fertilization action based on specific vegetative needs to improve vegetable production. The proposed interface was implemented using Robot Operating System (ROS) and allows: visualizing the states of the robot within the crop by using a highly realistic environment developed in Unity3D and shows specific information of the plants’ vegetative data fertilization needs and suggests the user take action. The tests to validate the method have been carried out in the fields of the ETSIAAB-UPM. According to the multi-spectral data taken after (2 weeks after being planted) and before (3 months after growth), main results have shown that NDVI indexes mean values in the row crop vegetables have normal levels around 0.4 concerning initial NDVI values, and its growth was homogeneous, validating the influence of ROBOFERT.

Keywords

  • Virtual reality
  • ROS
  • Robotics
  • Precision agriculture
  • Human machine interface
  • Image processing

Supported by European project “Sureveg: Strip-croppingand recycling for biodiverse and resource-efficient in-tensive vegetable production”, belonging to the actionERA-net CORE Organic Cofund: https://projects.au.dk/coreorganiccofund/.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-96147-3_5
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-96147-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

References

  1. Adamides, G., et al.: HRI usability evaluation of interaction modes for a teleoperated agricultural robotic sprayer. Appl. Ergon. 62, 237–246 (2017)

    Google Scholar 

  2. Berenstein, R., Edan, Y., Halevi, I.B.: A remote interface for a human-robot cooperative vineyard sprayer. In: Proceedings International Social Precision Agriculture (ICPA), pp. 15–18 (2012)

    Google Scholar 

  3. Berenstein, R., Edan, Y.: Human-robot collaborative site-specific sprayer. J. Field Robot. 34(8), 1519–1530 (2017)

    CrossRef  Google Scholar 

  4. Cardim Ferreira Lima, M., Krus, A., Valero, C., Barrientos, A., del Cerro, J., Roldán-Gómez, J.J.: Monitoring plant status and fertilization strategy through multispectral images. Sensors 20(2) (2020). https://doi.org/10.3390/s20020435. https://www.mdpi.com/1424-8220/20/2/435

  5. Carruth, D.W., Hudson, C., Fox, A.A., Deb, S.: User interface for an immersive virtual reality greenhouse for training precision agriculture. In: Chen, J.Y.C., Fragomeni, G. (eds.) International Conference on Human-Computer Interaction, pp. 35–46. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49698-2_3

  6. Cofund, C.O.: Sureveg project. https://projects.au.dk/coreorganiccofund/core-organic-cofund-projects/sureveg/. Accessed 27 Aug (2020)

  7. Cruz Ulloa, C., Krus, A., Barrientos, A., Del Cerro, J., Valero, C.: Robotic fertilisation using localisation systems based on point clouds in strip-cropping fields. Agronomy 11(1) (2021). https://doi.org/10.3390/agronomy11010011. https://www.mdpi.com/2073-4395/11/1/11

  8. Cruz Ulloa, C., Krus, A., Barrientos, A., Del Cerro, J., Valero, C.: Robotic fertilisation using localisation systems based on point clouds in strip-cropping fields. Agronomy 11(1), 11 (2021)

    CrossRef  Google Scholar 

  9. Durmuş, H., Güneş, E.O., Kırcı, M., Üstündağ, B.B.: The design of general purpose autonomous agricultural mobile-robot: “agrobot”. In: 2015 Fourth International Conference on Agro-Geoinformatics (Agro-geoinformatics), pp. 49–53. IEEE (2015)

    Google Scholar 

  10. Emmi, L., Paredes-Madrid, L., Ribeiro, A., Pajares, G., Gonzalez-de Santos, P.: Fleets of robots for precision agriculture: a simulation environment. Ind. Robot Int. J. (2013)

    Google Scholar 

  11. Huuskonen, J., Oksanen, T.: Soil sampling with drones and augmented reality in precision agriculture. Comput. Electron. Agricul. 154, 25–35 (2018)

    CrossRef  Google Scholar 

  12. Jeon, H.Y., Tian, L.F., Grift, T.E.: Development of an individual weed treatment system using a robotic arm. In: 2005 ASAE Annual Meeting. p. 1. American Society of Agricultural and Biological Engineers (2005)

    Google Scholar 

  13. Ji, W., Zhao, D., Cheng, F., Xu, B., Zhang, Y., Wang, J.: Automatic recognition vision system guided for apple harvesting robot. Comput. Electrical Eng. 38(5), 1186–1195 (2012). https://doi.org/10.1016/j.compeleceng.2011.11.005. https://www.sciencedirect.com/science/article/pii/S0045790611001819. special issue on Recent Advances in Security and Privacy in Distributed Communications and Image processing

  14. Krus, A., van Apeldoorn, D., Valero, C., Ramirez, J.J.: Acquiring plant features with optical sensing devices in an organic strip-cropping system. Agronomy 10(2) (2020). https://doi.org/10.3390/agronomy10020197. https://www.mdpi.com/2073-4395/10/2/197

  15. Lee, W.S., Slaughter, D., Giles, D.: Robotic weed control system for tomatoes. Precis Agricul. 1(1), 95–113 (1999)

    CrossRef  Google Scholar 

  16. Lin, T.T., Hsiung, Y.K., Hong, G.L., Chang, H.K., Lu, F.M.: Development of a virtual reality GIS using stereo vision. Comput. Electron. Agricult. 63(1), 38–48 (2008)

    CrossRef  Google Scholar 

  17. Milioto, A., Lottes, P., Stachniss, C.: Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. In: 2018 IEEE international conference on robotics and automation (ICRA), pp. 2229–2235. IEEE (2018)

    Google Scholar 

  18. Murakami, N., et al.: Development of a teleoperation system for agricultural vehicles. Comput. Electron. Agricult. 63(1), 81–88 (2008)

    CrossRef  Google Scholar 

  19. Nigam, A., Kabra, P., Doke, P.: Augmented reality in agriculture. In: 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 445–448. IEEE (2011)

    Google Scholar 

  20. Ohi, N., et al.: Design of an autonomous precision pollination robot. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7711–7718 (2018). https://doi.org/10.1109/IROS.2018.8594444

  21. Paoletti, M.E., Haut, J.M., Plaza, J., Plaza, A.: Estudio comparativo de técnicas de clasificación de imágenes hiperespectrales. Revista Iberoamericana de Automática e Informática Industrial 16(2), 129–137 (2019)

    CrossRef  Google Scholar 

  22. Quaglia, G., Visconte, C., Scimmi, L.S., Melchiorre, M., Cavallone, P., Pastorelli, S.: Design of a UGV powered by solar energy for precision agriculture. Robotics 9(1) (2020). https://doi.org/10.3390/robotics9010013, https://www.mdpi.com/2218-6581/9/1/13

  23. Roldán-Gómez, J., de León Rivas, J., Garcia-Aunon, P., Barrientos, A.: Una revisión de los sistemas multi-robot: Desafíos actuales para los operadores y nuevos desarrollos de interfaces. Revista Iberoamericana de Automática e Informática Industrial 17(3) (2020)

    Google Scholar 

  24. Rovira-Más, F., Zhang, Q., Reid, J.F.: Stereo vision three-dimensional terrain maps for precision agriculture. Comput. Electron. Agricul. 60(2), 133–143 (2008)

    CrossRef  Google Scholar 

  25. Sahu, Y., Sharma, S., Kumar, V., Kumar, T.: Wireless pc control robot using 8051 micro controller and RF module. Int. J. Eng. Techn. Res. 7(3)

    Google Scholar 

  26. Santana-Fernández, J., Gómez-Gil, J., del Pozo-San-Cirilo, L.: Design and implementation of a GPS guidance system for agricultural tractors using augmented reality technology. Sensors 10(11), 10435–10447 (2010)

    CrossRef  Google Scholar 

  27. Gonzalez-de-Santos, P., et al.: Fleets of robots for environmentally-safe pest control in agriculture. Precis. Agricul. 18(4), 574–614 (2016). https://doi.org/10.1007/s11119-016-9476-3

    CrossRef  Google Scholar 

  28. Slaughter, D., Giles, D., Downey, D.: Autonomous robotic weed control systems: a review. Comput. Electron. Agricul. 61(1), 63–78 (2008)

    CrossRef  Google Scholar 

  29. Tapia, E.P.: Interfaz inmersiva para misiones robóticas basada en realidad virtual (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christyan Cruz Ulloa .

Editor information

Editors and Affiliations

Appendix

Appendix

Video of the field execution of the fertilization process with the developed interface. https://www.youtube.com/watch?v=xOamJDMgjGY.

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Cruz Ulloa, C., Krus, A., Torres Llerena, G., Barrientos, A., Del Cerro, J., Valero, C. (2022). ROBOFERT: Human - Robot Advanced Interface for Robotic Fertilization Process. In: Botto-Tobar, M., S. Gómez, O., Rosero Miranda, R., Díaz Cadena, A., Montes León, S., Luna-Encalada, W. (eds) Trends in Artificial Intelligence and Computer Engineering. ICAETT 2021. Lecture Notes in Networks and Systems, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-96147-3_5

Download citation