Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
Source abstract
Abstract
The use of information and communication technologies (ICT) in agriculture is far from their potential. In this article, we consider how to facilitate and systematize the process of transforming traditional agriculture into digital agriculture; Agriculture 4.0. Among the different technologies, we focus on the IoT aspects. In the article, we propose a new approach for the design of intelligent agricultural management and supervision systems. The proposed approach is illustrated as an example of application in the beekeeping sector. Indeed, this sector is affected by a crisis due to the disappearance of bees and the different actors need support to make their decisions. As an example of decisions that can be made, we can cite: treatment planning or policy planning. An architecture based on sensors and open data is proposed to help them make decisions. An implementation of it is shown; it is based on a device with sensors, as well as an interface to collect the data on beehives and show notifications and alerts to beekeepers. The proposed architecture is flexible, and it can be used in the context of different levels of technology maturity. The final objective is to develop a reusable architecture for Agriculture 4.0.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Insect and Arachnid Ecology and Behavior
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
EFREI Paris, Efrei Research Lab, 30/32 Avenue de la République, 94800 Villejuif, France
Mines ParisTech, PSL University, Centre de Recherche en Informatique, 35 rue Saint Honoré, 77300 Fontainebleau, France
Funding
French Chambers of Agriculture development programs (CASDAR) · EFREI Paris (engineering school of digital technologies)
References (49)
Show source-provided references
1. Latioui, Z.E., Bougueroua, L., and Moretto, A. (2018, January 13–15). Social media chatbot system-beekeeping case study. Proceedings of the International Conference on Hybrid Intelligent Systems, Porto, Portugal.10.1007/978-3-030-14347-3_29 ↗
2. (2022, September 28). FranceAgreMer. Bilan de Campagne Miel 2020; Technical Report; FranceAgreMer. Available online: https://www.franceagrimer.fr/content/download/67599/document/BIL_API_2021_Bilan_campagne_2020.pdf.
3. Classification of cyber-physical production systems applications: Proposition of an analysis framework10.1016/j.compind.2018.10.002 ↗
4. The main trends of digitalization in agriculture10.1088/1755-1315/981/3/032018 ↗
5. Schallmo, D.R., and Williams, C.A. (2018). Digital Transformation Now! Guiding the Successful Digitalization of Your Business Model, Springer.10.1007/978-3-319-72844-5_3 ↗
6. Digital transformation: Opportunities to create new business models10.1108/10878571211209314 ↗
7. Gabriel, A., and Gandorfer, M. (2022). Adoption of digital technologies in agriculture—An inventory in a european small-scale farming region. Precision Agriculture, Springer.10.1007/s11119-022-09931-1 ↗
8. Digital Innovation Management: Reinventing Innovation Management Research in a Digital World10.25300/MISQ/2017/41:1.03 ↗
9. Digital Transformation10.1109/MS.2018.2801537 ↗
10. Verina, N., and Titko, J. (2019, January 9–10). Digital transformation: Conceptual framework. Proceedings of the International Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering”, Vilnius, Lithuania.10.3846/cibmee.2019.073 ↗
11. The industrial management of SMEs in the era of Industry 4.010.1080/00207543.2017.1372647 ↗
12. Danjou, C., Rivest, L., and Pellerin, R. (2017, January 3–5). Douze positionnements stratégiques pour l’Industrie 4.0: Entre processus, produit et service, de la surveillance à l’autonomie (Twelve strategic positioning for Industry 4.0: Between process, product and service, from monitoring to autonomy). Proceedings of the 12e Congrès International de Génie Industriel CIGI, Compiègne, France.
13. From industry 4.0 to agriculture 4.0: A framework to manage product data in agri-food supply chain for voluntary traceability
14. Ahmed, S.H., Kim, G., and Kim, D. (2013, January 13–15). Cyber Physical System: Architecture, applications and research challenges. Proceedings of the 2013 IFIP Wireless Days (WD), Valencia, Spain.10.1109/WD.2013.6686528 ↗
15. Internet of Things (IoT): A vision, architectural elements, and future directions. Including Special sections: Cyber-enabled Distributed Computing for Ubiquitous Cloud and Network Services & Cloud Computing and Scientific Applications—Big Data, Scalable Analytics, and Beyond10.1016/j.future.2013.01.010 ↗
16. The Internet of Things: A survey10.1016/j.comnet.2010.05.010 ↗
17. Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics10.1016/j.future.2017.09.016 ↗
18. Vincent, D.R., Deepa, N., Elavarasan, D., Srinivasan, K., Chauhdary, S.H., and Iwendi, C. (2019). Sensors driven AI-based agriculture recommendation model for assessing land suitability. Sensors, 19.10.3390/s19173667 ↗
19. Review of IoT applications in agro-industrial and environmental fields10.1016/j.compag.2017.09.015 ↗
20. Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Randazzo, G., and Muzirafuti, A. (2022). IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci., 12.10.3390/app12073396 ↗
21. Colombo-Mendoza, L.O., Paredes-Valverde, M.A., Salas-Zárate, M.d.P., and Valencia-García, R. (2022). Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain. Appl. Sci., 12.10.20944/preprints202201.0445.v1 ↗
22. García, L., Parra, L., Jimenez, J.M., Lloret, J., and Lorenz, P. (2020). IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture. Sensors, 20.10.3390/s20041042 ↗
23. A Novel Design and Implementation of Data Acquisition and Preprocessing System for Precision Agriculture
24. López-Morales, J.A., Martínez, J.A., and Skarmeta, A.F. (2020). Digital Transformation of Agriculture through the Use of an Interoperable Platform. Sensors, 20.10.3390/s20041153 ↗
25. Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies10.1109/JAS.2021.1003925 ↗
26. Felch, V., Asdecker, B., and Sucky, E. (2019, January 8–11). Maturity Models in the Age of Industry 4.0–Do the Available Models Correspond to the Needs of Business Practice?. Proceedings of the 52nd Hawaii International Conference on System Sciences, Maui, HI, USA.10.24251/HICSS.2019.620 ↗
27. Menon, K., Kärkkäinen, H., and Lasrado, L.A. (July, January 27). Towards a Maturity Modeling Approach for the Implementation of Industrial Internet. Proceedings of the PACIS 2016; Association for Information Systems, AIS Electronic Library (AISeL), Chiayi, Taiwan.
28. Lödding, H., Riedel, R., Thoben, K.D., von Cieminski, G., and Kiritsis, D. (2017, January 3–7). The IoT Technological Maturity Assessment Scorecard: A Case Study of Norwegian Manufacturing Companies. Proceedings of the Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing, Hambourg, Germany.10.1007/978-3-319-66923-6 ↗
29. Westermann, T., Anacker, H., Dumitrescu, R., and Czaja, A. (2016, January 3–5). Reference architecture and maturity levels for cyber-physical systems in the mechanical engineering industry. Proceedings of the 2016 IEEE International Symposium on Systems Engineering (ISSE), Edinburgh, UK.10.1109/SysEng.2016.7753153 ↗
30. Büyük, A.M., Ateş, G., Burghli, S., Yılmaz, D., Temur, G.T., and Sivri, Ç. (2020, January 24–26). Digital Maturity Assessment Model for Smart Agriculture. Proceedings of the The International Symposium for Production Research, Antalya, Turkey.10.1007/978-3-030-62784-3_24 ↗
31. Estimation of Honey Production in Beekeeping Enterprises from Eastern Part of Turkey through Some Data Mining Algorithms10.17582/journal.pjz/2018.50.6.2199.2207 ↗
32. OSEMN process for working over data acquired by IoT devices mounted in beehives
33. A method for mining combined data from in-hive sensors, weather and apiary inspections to forecast the health status of honey bee colonies10.1016/j.compag.2019.105161 ↗
34. (2022, September 28). Bee Informed Partnership—Using Beekeepers’ Real World Experience to Solve Beekeepers’ Real World Problems. Available online: https://beeinformed.org/.
35. b+WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring10.1016/j.compag.2016.04.008 ↗
36. Decision support system for temperature monitoring in beehives10.5937/AASer1642135M ↗
37. Balta, A., Dogan, S., Ozmen Koca, G., and Akbal, E. (2017, January 10–12). Software Modeling of Remote Controlled Beehive Design. Proceedings of the International Conference on Advances and Innovations in Engineering ICAIE, Elazig, Turkey.
38. Zogović, N., Mladenović, M., and Rašić, S. (2017, January 13–16). From primitive to cyber-physical beekeeping. Proceedings of the 7th International Conference on Information Society and Technology, Kopaonik, Serbia.
39. Cazier, J. (2022, September 28). The Genius Hive Will Be Able to Tell You What It Needs to Do Better. Available online: https://www.beeculture.com/peering-into-the-future-a-path-to-the-genius-hive/.
40. Mrozek, D., Gȯrny, R., Wachowicz, A., and Małysiak-Mrozek, B. (2021). Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning. Appl. Sci., 11.10.3390/app112211078 ↗
41. Web Monitoring of Bee Health for Researchers and Beekeepers Based on the Internet of Things10.1016/j.procs.2018.04.103 ↗
42. Zetterman, B.E.A. (2018). Beekeepers Usage of IoT: Data Collection, Sharing and Visualization in the Domain of Beekeeping. [Master’s Thesis, Linnaeus University].
43. Social Media Chatbot System—Beekeeping Case Study10.1007/978-3-030-14347-3_29 ↗
44. Chabrol, M., Gourgand, M., and Rodier, S. (2008, January 3–6). A modeling methodology and its application to the design of decision-making aid tools dedicated to the hospital systems. Proceedings of the RCIS’08: International Conference on Research Challenges in Information, Marrakech, Morocco.10.1109/RCIS.2008.4632103 ↗
45. A combined financial and physical flows evaluation for logistic process and tactical production planning: Application in a company supply chain. Special Section on Recent Developments in the Design, Control, Planning and Scheduling of Productive Systems10.1016/j.ijpe.2007.01.012 ↗
46. Huet, J.C., Bougueroua, L., Kriouile, Y., and Moretto, A. (2020, January 12–15). Toward an intelligent system architecture for smart agriculture: Application to smart beehives. Proceedings of the 20th International Conference on Intelligent Systems Design and Applications—ISDA 2020, Online.
47. McLeod, R. (1989). Information Systems, Prentice Hall PTR. [1st ed.].
48. ETSI (2022, September 28). Context Information Management (CIM); NGSI-LD API. Technical Report; ETSI. Available online: https://www.etsi.org/deliver/etsi_gs/CIM/001_099/009/01.03.01_60/gs_CIM009v010301p.pdf.
49. Complementing IoT Services Through Software Defined Networking and Edge Computing: A Comprehensive Survey10.1109/COMST.2020.2997475 ↗