Skip to main content

LambdAgrIoT: a new architecture for agricultural autonomous robots’ scheduling: from design to experiments

This article has been updated


The usage of IoT and robots is more and more present in smart farming, and in particular in agro-ecology since robots are able to provide smart practices and avoid repetitive human tasks. However, these new technologies rise several research issues, which are strongly inter-related, about Farm Management Information System, such as robots’ programming, sensor data capture, management and processing at different layers of the IoT ecosystem. In particular, scheduling the tasks of different autonomous agricultural robots needs for a complex architecture that support at the same time real-time monitoring of robots and analysis of their historical data (Belhassena et al., Towards an architecture for agricultural autonomous robots’ scheduling. In: 2021 IEEE 25th international enterprise distributed object computing workshop (EDOCW), 2021. IEEE Computer Society, Los Alamitos, pp 194–203, 2021, Many studies investigated these issues, but to the best of our knowledge none has contributed with a fully-featured architecture design of monitoring and scheduling of autonomous agricultural robots. This work extends our previous work, where we propose a new architecture for autonomous agriculture robots scheduling, called LambdAgrIoT. LambdAgrIoT is designed to support big data and different types of workload (real-time, near real-time, analytic, and CRUD). We present the main features of each layer, and the implementation details. We also put to the test our LambdAgrIoT architecture using simulated data, and providing a real experience in a field. Results from real experiments show the feasibility of our new proposal.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Data availability

The datasets generated during and/or analysed during the current study are not publicly available due to confidential reasons.

Change history

  • 13 November 2022

    The original online version of this article was revised: The author biographies and photos were mismatched, the biographies and photos have been corrected now.




  1. Afrin, M., Jin, J., Rahman, A., et al.: Resource allocation and service provisioning in multi-agent cloud robotics: a comprehensive survey. IEEE Commun. Surv. Tutor. (2021).

    Article  Google Scholar 

  2. Aissi, H., Bazgan, C., Vanderpooten, D.: Min–max and min–max regret versions of combinatorial optimization problems: a survey. Eur. J. Oper. Res. 197(2), 427–438 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  3. Alsahfi, T., Almotairi, M., Elmasri, R.: A survey on trajectory data warehouse. Spat. Inf. Res. 28(1), 53–66 (2020)

    Article  Google Scholar 

  4. Arooj, A., Farooq, M.S., Akram, A., et al.: Big data processing and analysis in Internet of vehicles: architecture, taxonomy, and open research challenges. Arch. Comput. Methods Eng. 29, 793–829 (2021)

    Article  Google Scholar 

  5. Ayaz, M., Ammad-Uddin, M., Sharif, Z., et al.: Internet-of-Things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access 7(1), 129551–129583 (2019)

    Article  Google Scholar 

  6. Bechtsis, D., Moisiadis, V., Tsolakis, N., et al.: Scheduling and control of unmanned ground vehicles for precision farming: a real-time navigation tool. In: International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA), 2017, pp. 180–187 (2017)

  7. Belhassena, A., Battistoni, P., Souza, M., et al.: Towards an architecture for agricultural autonomous robots’ scheduling. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), 2021, pp 194–203. IEEE Computer Society, Los Alamitos (2021).

  8. Belhassena, A., Bimonte, S., Battistoni, P., et al.: On modeling data for IoT agroecology applications by means of a UML profile. In: International Conference on Management of Digital EcoSystems (MEDES), 2021 (2021)

  9. Belhassena, A., Bimonte, S., Battistoni, P., et al.: On modeling data for IoT agroecology applications by means of a UML profile. In: Chbeir, R., Manolopoulos, Y., Bellatreche, L., et al. (eds) MEDES ’21: Proceedings of the 13th International Conference on Management of Digital EcoSystems, Virtual Event, Tunisia, 1–3 November 2021, pp 120–128. ACM (2021).

  10. Bimonte, S.: Current approaches, challenges, and perspectives on spatial OLAP for agri-environmental analysis. Int. J. Agric. Environ. Inf. Syst. 7(4), 32–49 (2016).

    Article  Google Scholar 

  11. Bimonte, S., Edoh-Alove, É., Coulibaly, F.A.: Map4OLAP: a web-based tool for interactive map visualization of OLAP queries. In: Chen, Y., Ludwig, H., Tu, Y., et al. (eds) 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 15–18 December 2021, pp 3747–3750. IEEE (2021).

  12. Bochtis, D.D., Sørensen, C.G., Busato, P.: Advances in agricultural machinery management: a review. Biosyst. Eng. 126, 69–81 (2014)

    Article  Google Scholar 

  13. Card, S.K., Mackinlay, J.D., Shneiderman, B.: Readings in Information Visualization—Using Vision to Think. Academic, London (1999)

    Google Scholar 

  14. Cariou, C., Gobor, Z., Seiferth, B., et al.: Mobile robot trajectory planning under kinematic and dynamic constraints for partial and full field coverage. J. Field Robot. 34(7), 1297–1312 (2017)

    Article  Google Scholar 

  15. Cordeau, J.F., Desaulniers, G., Desrousiers, J., et al.: VRP with time windows. In: The Vehicle Routing Problem. SIAM Monographs on Discrete Mathematics and Applications, pp. 157–193. SIAM, Philadelphia (2002)

  16. Dalgaard, T., Hutchings, N., Porter, J.: Agroecology, scaling and interdisciplinarity. Agric. Ecosyst. Environ. 100(1), 39–51 (2003)

    Article  Google Scholar 

  17. Debauche, O., El Moulat, M., Mahmoudi, S., et al.: Irrigation pivot-center connected at low cost for the reduction of crop water requirements. In: International Conference on Advanced Communication Technologies and Networking (CommNet), 2018, pp 1–9. IEEE (2018)

  18. Deremetz, M., Couvent, A., Lenain, R., et al.: A generic control framework for mobile robots edge following. In: Proceedings of International Conference on Informatics in Control, Automation and Robotics, 2019, pp 104–113 (2019)

  19. Dobbelaere, P., Esmaili, K.S.: Kafka versus RabbitMQ: a comparative study of two industry reference publish/subscribe implementations: industry paper. In: ACM International Conference on Distributed and Event-based Systems (DEBS), 2017, pp. 227–238 (2017)

  20. Edwards, G.T., Hinge, J., Skou-Nielsen, N., et al.: Route planning evaluation of a prototype optimised infield route planner for neutral material flow agricultural operations. Biosyst. Eng. 153, 149–157 (2017)

    Article  Google Scholar 

  21. Emani, C.K., Cullot, N., Nicolle, C.: Understandable big data: a survey. Comput. Sci. Rev. 17, 70–81 (2015)

    Article  MathSciNet  Google Scholar 

  22. Emmi, L., Gonzalez-de Soto, M., Pajares, G., et al.: New trends in robotics for agriculture: integration and assessment of a real fleet of robots. Sci. World J. (2014).

    Article  Google Scholar 

  23. Fountas, S., Carli, G., Sørensen, C.G., et al.: Farm management information systems: current situation and future perspectives. Comput. Electron. Agric. 115, 40–50 (2015)

    Article  Google Scholar 

  24. Gonzalez-de Santos, P., Fernández, R., Sepúlveda, D., et al.: Field robots for intelligent farms—inhering features from industry. Agronomy 10(11), 1638 (2020)

    Article  Google Scholar 

  25. Hesse, G., Matthies, C., Uflacker, M.: How fast can we insert? An empirical performance evaluation of Apache Kafka. In: IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2020, pp 641–648 (2020)

  26. Iftikhar, N., Lachowicz, B.P., Madarasz, A., et al.: Real-time visualization of sensor data in smart manufacturing using Lambda architecture. In: International Conference on Data Science, Technology and Applications (DATA), 2020, pp. 215–222 (2020)

  27. Khorov, E., Kiryanov, A., Lyakhov, A., et al.: A tutorial on IEEE 802.11ax high efficiency WLANs. IEEE Commun. Surv. Tutor. (2019).

    Article  Google Scholar 

  28. Krishnan, A., Swarna, S., Balasubramanya, S.H.: Robotics, IoT, and AI in the automation of agricultural industry: a review. In: Proceedings of B-HTC, 2020, pp. 1–6 (2020)

  29. Lujak, M., Sklar, E., Semet, F.: Agriculture fleet vehicle routing: a decentralised and dynamic problem. AI Commun. 34(1), 55–71 (2021)

    Article  MathSciNet  Google Scholar 

  30. Luo, X., Zhang, L.: The optimal scheduling model for agricultural machinery resources with time-window constraints. Int. J. Simul. Model. 15(4), 721–731 (2016)

    Article  Google Scholar 

  31. MacEachren, A.M., Gahegan, M., Pike, W., et al.: Geovisualization for knowledge construction and decision support. IEEE Comput. Graph. Appl. 24(1), 13–17 (2004)

    Article  Google Scholar 

  32. Mahale, R.B., Sonavane, S.: Smart poultry farm monitoring using IoT and wireless sensor networks. Int. J. Adv. Res. Comput. Sci. (2016).

    Article  Google Scholar 

  33. Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning, New York (2015)

    Google Scholar 

  34. Maurel, V.B., Huyghe, C.: Putting agricultural equipment and digital technologies at the cutting edge of agroecology. Ol. Corps Gras Lipides 24(3), 1–7 (2017)

    Google Scholar 

  35. Narayan, S., Jayawardena, C., Wang, J., et al.: Performance test of IEEE 802.11ac wireless device. In: International Conference on Computer Communication and Informatics, 2015 (2015).

  36. Navarro, E., Costa, N., Pereira, A.: A systematic review of IoT solutions for smart farming. Sensors 20(15), 4231 (2020)

    Article  Google Scholar 

  37. Nukala, R., Panduru, K., Shields, A., et al.: Internet of Things: a review from ‘farm to fork’. In: Irish Signals and Systems Conference (ISSC), 2016, pp. 1–6 (2016)

  38. Pandya, A., Odunsi, O., Liu, C., et al.: Adaptive and efficient streaming time series forecasting with Lambda architecture and Spark. In: IEEE International Conference on Big Data, 2020, pp. 5182–5190. IEEE (2020)

  39. Percivall, G.: Realizing the geospatial potential of mobile, IoT and big data. In: Liao, L. (ed.) International Conference on Computing for Geospatial Research and Application, 2012, p. 8. ACM (2012)

  40. Pinedo, M., Zacharias, C., Zhu, N.: Scheduling in the service industries: an overview. J. Syst. Sci. Syst. Eng. 24(1), 1–48 (2015)

    Article  Google Scholar 

  41. Ribeiro de Almeida, D., de Souza, B.C., Gomes de Andrade, F., et al.: A survey on big data for trajectory analytics. ISPRS Int. J. Geo-Inf. 9(2), 88 (2020)

    Article  Google Scholar 

  42. Rossit, D.A., Tohmé, F., Frutos, M.: Industry 4.0: smart scheduling. Int. J. Prod. Res. 57(12), 3802–3813 (2019)

    Article  Google Scholar 

  43. Roukh, A., Fote, F.N., Mahmoudi, S.A., et al.: Big data processing architecture for smart farming. Procedia Comput. Sci. 177, 78–85 (2020)

    Article  Google Scholar 

  44. Saint-Guillain, M., Deville, Y., Solnon, C.: A multistage stochastic programming approach to the dynamic and stochastic VRPTW. In: Michel, L. (ed.) International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 2015, pp. 357–374 (2015)

  45. Seyyedhasani, H., Dvorak, J.S.: Dynamic rerouting of a fleet of vehicles in agricultural operations through a dynamic multiple depot vehicle routing problem representation. Biosyst. Eng. 171, 63–77 (2018)

    Article  Google Scholar 

  46. Solomon, M.M.: Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35(2), 254–265 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  47. Villa-Henriksen, A., Edwards, G.T., Pesonen, L.A., et al.: Internet of Things in arable farming: implementation, applications, challenges and potential. Biosyst. Eng. 191, 60–84 (2020).

    Article  Google Scholar 

  48. Vitali, G., Francia, M., Golfarelli, M., et al.: Crop management with the IoT: an interdisciplinary survey. Agronomy 11(1) (2021).

  49. Wellington, C., Courville, A., Stentz, A.T.: A generative model of terrain for autonomous navigation in vegetation. Int. J. Robot. Res. 25(12), 1287–1304 (2006)

    Article  Google Scholar 

  50. Wolfert, S., Ge, L., Verdouw, C., et al.: Big data in smart farming—a review. Agric. Syst. 153, 69–80 (2017)

    Article  Google Scholar 

  51. Wulfsohn, D., Zamora, F.A., Téllez, C.P., et al.: Multilevel systematic sampling to estimate total fruit number for yield forecasts. Precis. Agric. 13(2), 256–275 (2012)

    Article  Google Scholar 

  52. Zaharia, M., Chowdhury, M., Das, T., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: USENIX Symposium on Networked Systems Design and Implementation, 2012, pp. 15–28 (2012)

  53. Zaharia, M., Xin, R.S., Wendell, P., et al.: Apache Spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)

    Article  Google Scholar 

  54. Zhai, Z., Martínez, J.F., Beltran, V., et al.: Decision support systems for agriculture 4.0: survey and challenges. Comput. Electron. Agric. (2020).

    Article  Google Scholar 

Download references


This work is supported by the French National Research Agency Project ANR-19-LCV2-0011 Tiara, and French Government IDEX-ISITE Initiative 16-IDEX-0001 (CAP 20-25).

Author information

Authors and Affiliations



All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. All authors write the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Geraldine André.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This paper extends our previous EAIoT 2021 paper [7].

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

André, G., Bachelet, B., Battistoni, P. et al. LambdAgrIoT: a new architecture for agricultural autonomous robots’ scheduling: from design to experiments. Cluster Comput 26, 2993–3015 (2023).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: