Validation of Automated Farming

  • M. RookerEmail author
  • J. F. López
  • P. Horstrand
  • M. Pusenius
  • T. Leppälampi
  • R. Lattarulo
  • J. Pérez
  • Z. Slavik
  • S. Sáez
  • L. Andreu
  • A. Ruiz
  • D. Pereira
  • L. Zhao


This chapter presents first concepts for the improved validation of automated farming solutions. Within the ENABLE-S3 ECSEL JU project, the farming use case team presents developments within the agricultural domain, that can in the future improve the life and working environment of farmers. Applications such as autonomous driving of farming vehicles equipped with sensors and drones supporting hyperspectral cameras, validated by newly defined testing systems like co-simulation of farming vehicles, model-based simulation of farming systems and verification and testing of in-vehicle communication are advances developed during the project. As agricultural activities are very dependent on environmental parameters (e.g. weather, harvest ripeness) and the availability of the actual vehicles (which is very often not the case), the use case team opted for realistic simulators for first validation approaches. In this work, multiple simulators are introduced that combine many agricultural concepts including the simulation of the farming systems (i.e. harvester, tractors and drones). Additionally, introducing autonomy into vehicles requires deterministic in-vehicle communication and the guarantee that messages arrive timely. Validation of in-vehicle communication is introduced to showcase the applicability of the technology. The overall goal of the work performed in this use case is to reduce the testing costs and time of farming scenarios, be less dependent on many factors (like crop availability) and be able to perform continuous validation and verification of the farming systems.


Farming Agriculture Verification Validation Hyperspectral Trajectory planning Sensors Co-simulation Model-based simulation Communication Timing behavior ENABLE S3 


  1. 1.
    Bell, T., Elkaim, G., Parkinson, B.: Automatic steering of farm vehicles using GPS. In: FAO International Conference on Precision Agriculture (1996)Google Scholar
  2. 2.
    Lenain, R., Thuilot, B., Cariou, C., Martinet, P.: Adaptive and predictive non-linear control for sliding vehicle guidance: application to trajectory tracking of farm vehicles relying on a single RTK GPS. In: IEEE International Conference on Intelligent Robots and Systems, pp. 455–460 (2004)Google Scholar
  3. 3.
    Cariou, C., Lenain, R., Thuilot, B., Berducat, M.: Automatic guidance of a four-wheel-steering mobile robot for accurate field operations. J. Field Rob. 26, 504–518 (2009)CrossRefGoogle Scholar
  4. 4.
    Lattarulo, R., Pérez, J., Dendaluce, M.: A complete framework for developing and testing automated driving controllers. In: IFAC World Congress 2017, pp. 258–263 (2017)CrossRefGoogle Scholar
  5. 5.
    Gonzalo, J., López, D., Domínguez, D., Gracía, A., Escapa, A.: On the capabilities and limitations of high altitude pseudo-satellites. Prog. Aerosp. Sci. 98, 37–56 (2018)CrossRefGoogle Scholar
  6. 6.
    Chang, C.-I.: Hyperspectral Imaging. Springer, New York (2003)CrossRefGoogle Scholar
  7. 7.
    Lopez, S., Vladimirova, T., Gonzalez, C., Resano, J., Mozos, D., Plaza, A.: The promise of reconfigurable computing for hyperspectral imaging onboard systems: a review and trends. Proc. IEEE. 101(3), 698–728 (2013)CrossRefGoogle Scholar
  8. 8.
    Teke, M., Seda-Deveci, H., Haliloglu, O., Zubeyde-Gurbuz, S., Sakarya, U.: A short survey of hyperspectral remote sensing applications in agriculture. In: Proceedings of the 6th International Conference on Recent Advances in Space Technologies (RAST), Istanbul, Turkey, 12–14 June 2013Google Scholar
  9. 9.
    Sahoo, R.N., Ray, S.S., Manjunath, K.R.: Hyperspectral remote sensing of agriculture. Curr. Sci. 108(5), 848–859 (2015)Google Scholar
  10. 10.
    Adao, T., Hruska, J., Padua, L., Bessa, J., Peres, E., Morais, R., Joao-Sousa, J.: Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 9(11), 1110 (2017)CrossRefGoogle Scholar
  11. 11.
    Rodríguez, A.S., Horstrand, P., López, J.F., López, S.: Setting up an autonomous hyperspectral flying platform for precision agriculture. In: SPIE Remote Sensing, Berlin, Germany, 10–13 September 2018Google Scholar
  12. 12.
    Xue, J., Su, B.: Significant remote sensing vegetation indices: a review of development and applications. J. Sensors. 2017, 1353691 (2017)CrossRefGoogle Scholar
  13. 13.
    Thenkabail, P.S., Smith, R.B., De Pauw, E.: Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 71(2), 158–182 (2000)CrossRefGoogle Scholar
  14. 14.
    Huang, J., Wang, H., Dai, Q., Han, D.: Analysis of NDVI data for crop identification and yield estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(11), 4374–4384 (2014)CrossRefGoogle Scholar
  15. 15.
    Horstrand, P., López, S., López, J.F.: A novel implementation of a hyperspectral anomaly detection algorithm for real time applications with pushbroom sensors. In: IEEE 9th Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, Amsterdam, The Netherlands, 23–26 September 2018Google Scholar
  16. 16.
    Lattarulo, R., González, L., Martí, E., Matute, J., Marcano, M., Pérez, J.: Urban motion planning framework based on N-Bézier curves considering comfort and safety. J. Adv. Transp. 2018, 6060924 (2018)CrossRefGoogle Scholar
  17. 17.
    Azua, J.A.R., Boyer, M.: Complete modelling of AVB in network calculus framework. In: Proceedings of the 22nd International Conference on Real-Time Networks and Systems (2014)Google Scholar
  18. 18.
    Zhao, L., Pop, P., Zheng, Z., Li, Q.: Timing analysis of AVB traffic in TSN networks using network calculus. In: Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 25–36 (2018)Google Scholar
  19. 19.
    Pedro, A.: Dynamics contracts for verification and enforcement of real-time systems properties. PhD Thesis, Braga, Portugal, 10 April 2018Google Scholar
  20. 20.
    Pedro, A., Pereira, D., Pinto, J.S., Pinho, L.M.: Monitoring for a decidable fragment of MTLD. In: The 15th International Conference on Runtime Verification (RV’15), Vienna, Austria, 22–25 September 2015Google Scholar
  21. 21.
    Lakhneche, Y., Hooman, J.: Metric temporal logic with durations. Theor. Comput. Sci. 138(1), 169–199 (1995)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Pedro, A., Pereira, D., Pinto, J.S., Pinho, L.M.: RMTLD3Synth: runtime verification toolchain for generation of monitors based on the restricted metric temporal logic with durations. Accessed 23 Nov 2018
  23. 23.
    Pedro, A., Pereira, D., Pinto, J.S., Pinho, L.M.: Runtime verification of autopilot systems using a fragment of MTL-∫. Int. J. Softw. Tools Technol. Transfers (STTT). 20(4), 37–395 (2018)Google Scholar
  24. 24.
    Hospach, D., Müller, S., Rosenstiel, W., Bringmann, O.: Simulation of falling rain for robustness testing of video-based surround sensing systems. In: Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) (2016)Google Scholar
  25. 25.
    Pedro, A., Pereira, D., Pinto, J.S., Pinho, L.M.: The RMTLD3Synth web demonstrator. Accessed 23 Nov 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • M. Rooker
    • 1
    Email author
  • J. F. López
    • 2
  • P. Horstrand
    • 2
  • M. Pusenius
    • 3
  • T. Leppälampi
    • 3
  • R. Lattarulo
    • 4
  • J. Pérez
    • 4
  • Z. Slavik
    • 5
  • S. Sáez
    • 6
  • L. Andreu
    • 6
  • A. Ruiz
    • 6
  • D. Pereira
    • 7
  • L. Zhao
    • 8
  1. 1.TTTech Computertechnik AG, TTControl GmbHViennaAustria
  2. 2.Universidad de Las Palmas de Gran CanariaLas PalmasSpain
  3. 3.Creanex OyTampereFinland
  4. 4.TecnaliaDerioSpain
  5. 5.FZIKarlsruheGermany
  6. 6.ITIValènciaSpain
  7. 7.ISEPPortoPortugal
  8. 8.DTULyngbyDenmark

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