A Connected farm Metamodeling Using Advanced Information Technologies for an Agriculture 4.0

Loubna RabhiBrahim JabirNoureddine FalihLekbir AfraitesBelaid Bouikhalene

DOI 10.7160/aol.2023.150208
No 2/2023, June
pp. 93-104

Rabhi, L., Jabir, B., Falih, N., Afraites, L. and Bouikhalene, B. (2023) "A Connected farm Metamodeling Using Advanced Information Technologies for an Agriculture 4.0", AGRIS on-line Papers in Economics and Informatics, Vol. 15, No. 2, pp. 93-104. ISSN 1804-1930. DOI 10.7160/aol.2023.150208.

Abstract

The agriculture 4.0 revolution is an opportunity for farmers to meet the challenges in food production. It has become necessary to adopt a set of agricultural practices based on advanced technologies following the agriculture 4.0 revolution. This latter enables the creation of added value by combining innovative technologies: precision agriculture, information and communication technology, robotics, and Big Data. As an enterprise, a connected farm is also highly sensitive to strategic changes like organizational changes, changes in objectives, modified variety, new business objects, processes, etc. To strategically control its information system, we propose a metamodeling approach based on the ISO/IS 19440 enterprise meta-model, where we added some new constructs relating to new advanced digital technologies for Smart and Connected agriculture.

Keywords

Agriculture 4.0, metamodeling, advanced information technologies, digital agriculture, connected farm.

References

  1. Alex, S. A. and Kanavalli, A. (2019) "Intelligent Computational Techniques for Crops Yield Prediction and Fertilizer Management over Big Data Environment", International Journal of Innovative Technology and Exploring Engineering, Vol. 8, No. 12, pp. 3521-3526. E-ISSN 2278-3075. DOI 10.35940/ijitee.L2622.1081219.
  2. Bindu, C. A., Ravi, P. and Kumar, N. (2020) "Smart Fertilizer Distribution System with Crop Yield Prediction: A Review", International Advanced Research Journal in Science, Engineering and Technology, Vol. 7, No. 6, pp. 197-204. E-ISSN 2393-8021, ISSN 2394-1588 DOI 10.17148/IARJSET.2020.7627.
  3. Bondre, D. A. and Mahagaonkar, S. (2019) "Prediction of Crop Yield and Fertilizer Recommendation Using Machine Learning Algorithms", International Journal of Engineering Applied Sciences and Technology, Vol. 4, No. 5, pp. 371-376. E-ISSN 2455-2143 DOI 10.33564/ijeast.2019.v04i05.055.
  4. Cho Yongbeen, H. L. (2019) "The Bigdata Management and USe Case Study for Agriculture Based on Data (2019)", FFTC Agriculure Policy Plaform. [Online]. Available: https://ap.fftc.org.tw/ article/1630 [Accessed: Jan 15, 2023]
  5. Coble, K. H., Mishra, A. K., Ferrell, S. and Griffin, T. (2018) "Big data in agriculture: A challenge for the future", Applied Economic Perspectives and Policy, Vol. 40, No. 1, pp. 79-96. ISSN 1058-7195. DOI 10.1093/aepp/ppx056
  6. Dahikar, S. S., Extc, P. G. S. and College, S. (2015) "An Artificial Neural Network Approach for Agricultural Crop Yield Prediction Based on Various Parameters", International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), Vol. 4, No. 1, pp. 94-98. ISSN 2278-909X
  7. Falih, N., Jabir, B. and Rahmani, K. (2019) "Systemic approach for optimizing information technology resource as a contribution of information system governance", Indonesian Journal of Electrical Engineering and Computer Science, Vol. 14, No. 1, p. 135. E-ISSN 2502-4760, ISSN 2502-4752 DOI 10.11591/ijeecs.v14.i1.pp135-142.
  8. Fielke, S., Taylor, B. and Jakku, E. (2020) "Digitalisation of agricultural knowledge and advice networks: A state-of-the-art review", Agricultural Systems, Vol. 180 , p. 102763. ISSN 0308-521X DOI 10.1016/j.agsy.2019.102763..
  9. Jabir, B., Falih, N. Sarih, A. and Tannouche, A. (2021) “A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields", AGRIS on-line Papers in Economics and Informatics, Vol. 13, No. 1, pp. 49-57. ISSN 1804-1930 DOI 10.7160/aol.2021.130104.
  10. Jones, J. W., Antle, J. M., Basso, B., Boote, K. J., Conant, R. T., Foster, I., Godfray, H. C. J., Herrero, M., Howitt, R. E., Janssen, S., Keating, B. A., Munoz-Carpena, R., Porter, C. H., Rosenzweig, C. and Wheeler, T. R. (2017) "Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science", Agricultural Systems, Vol. 155, pp. 269-288. ISSN 0308-521X DOI 10.1016/j.agsy.2016.09.021.
  11. Kosanke, K., Vernadat, F. and Zelm, M. (2014) "Means to enable Enterprise Interoperation: CIMOSA Object Capability Profiles and CIMOSA Collaboration View", IFAC Proceedings Volumes, Vol. 47, No. 3, pp. 3292-3299. ISSN 1474-6670 DOI 10.3182/20140824-6-ZA-1003.01294.
  12. Lamine, E., Thabet, R., Sienou, A., Bork, D., Fontanili, F. and Pingaud, H. (2020) "BPRIM: An integrated framework for business process management and risk management", Computers in Industry, Vol. 117, p. 103199. ISSN 0166-3615 DOI 10.1016/j.compind.2020.103199.
  13. i, C. and Niu, B. (2020) "Design of smart agriculture based on big data and Internet of things", International Journal of Distributed Sensor Networks, Vol. 16, No. 5. E-ISSN, ISSN 1550-1329 DOI 10.1177/1550147720917065.
  14. Marcu, I., Voicu, C., Drăgulinescu, A. M. C., Fratu, O., Suciu, G., Balaceanu, C. and Andronache, M. M. (2019) "Overview of IoT basic platforms for precision agriculture", In: Poulkov, V. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures, FABULOUS 2019, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 283. Springer, Cham. E-ISBN 978-3-030-23976-3, ISBN 978-3-030-23975-6 DOI 10.1007/978-3-030-23976-3_13.
  15. Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A. and Nillaor, P. (2019) "IoT and agriculture data analysis for smart farm". Computers and Electronics in Agriculture, Vol. 156, pp. 467-474. E-ISSN 1872-7107, ISSN 0168-1699 DOI 10.1016/j.compag.2018.12.011.
  16. Naresh, V., Vatsala, B. R. and Raj, C. V. (2020) "Crop Yield Prediction and Fertilizer Recommendation", International Journal for Research in Engineering Application & Management, Vol. 10, No. 6, pp. 135-138. E-ISSN 2454-9150 DOI 10.35291/2454-9150.2020.0452.
  17. Ngo, V. M. (2020) "Crop Knowledge Discovery Based on Agricultural Big Data Integration", ICMLSC '20: Proceedings of the 4th International Conference on Machine Learning and Soft Computing, January 2020, Haiphong City Viet Nam. pp. 46-50. ISBN 978-1-4503-7631-0 DOI 10.1145/3380688.3380705.
  18. Perakis, K., Lampathaki, F., Nikas, K., Georgiou, Y., Marko, O. and Maselyne, J. (2020) "CYBELE – Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics", Computer Networks, Vol. 168, p. 107035. ISSN 138-1286 DOI 10.1016/j.comnet.2019.107035.
  19. Rabhi, L., Falih, N., Afraites, L. and Bouikhalene, B. (2021a) "A functional framework based on big data analytics for smart farming", Indonesian Journal of Electrical Engineering and Computer Science, Vol. 24, No. 3, pp. 1772-1779. E-ISSN 2502-4760, ISSN 2502-4752 DOI 10.11591/ijeecs.v24.i3.pp1772-1779.
  20. Rabhi, L., Falih, N., Afraites, L. and Bouikhalene, B. (2021b) "Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco", Indonesian Journal of Electrical Engineering and Computer Science, Vol. 24, No. 1, pp. 581-589. E-ISSN 2502-4760, ISSN 2502-4752 DOI 10.11591/ijeecs.v24.i1.pp581-589.
  21. Radhi, A. (2018) "Design and Implementation of a Smart Farm System Smart Farming is a modular platform made up of sensors that can be integrated", Association of Arab Universities Journal of Engineering Sciences, Vol. 24, No. 3, pp. 227-241. E-ISSN 2616-9401, ISSN 1726-4081
  22. Sung, J. (2018) "The Fourth Industrial Revolution and Precision Agriculture", In: Stephan, H. (ed) Automation in Agriculture - Securing Food Supplies for Future Generations. ISBN 978-953-51-3874-7 DOI 10.5772/intechopen.71582.
  23. Symeonaki, E., Arvanitis, K. and Piromalis, D. (2020) "A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0", Applied Sciences, Vol. 10, No. 3, pp. 1-35. ISSN 2076-3417 DOI 10.3390/app10030813.
  24. Triantafyllou, A., Sarigiannidis, P. and Bibi, S. (2019) "Precision agriculture: A remote sensing monitoring system architecture", Information, Vol. 10, No. 11, p. 348. ISSN 2078-2489 DOI 10.3390/info10110348.
  25. Triantafyllou, A., Tsouros, D. C., Sarigiannidis, P. and Bibi, S. (2019) "An architecture model for smart farming", Proceedings - 15th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2019, pp. 85-392 DOI 10.1109/DCOSS.2019.00081.
  26. Verma, G., Mittal, P. and Farheen, S. (2020) "Real Time Weather Prediction System Using IOT and Machine Learning", 6th International Conference on Signal Processing and Communication, ICSC 2020, pp. 322-324 DOI 10.1109/ICSC48311.2020.9182766.
  27. Vernadat, F. (2020) "Enterprise modelling: Research review and outlook", Computers in Industry, Vol. 122, No. 1-44. ISSN 0166-3615 DOI 10.1016/j.compind.2020.103265.
  28. Wang, B., Tao, F., Fang, X., Liu, C., Liu, Y. and Freiheit, T. (2021) "Smart Manufacturing and Intelligent Manufacturing: A Comparative Review", Engineering, Vol. 7, No. 6, pp. 738-757. ISSN 2095-8099 DOI 10.1016/j.eng.2020.07.017.
  29. Yang, F., Wang, K., Han, Y. and Qiao, Z. (2018) "A Cloud-Based Digital Farm Management System for Vegetable Production Process Management and Quality Traceability", Sustainability, Vol. 10, No. 11. ISSN 2071-1050 DOI 10.3390/su10114007.

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