# Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
## 7 Conclusions  
This paper presents a new approach for modelling, monitoring, and deploying Agriculture 4.0 systems. As for Industry 4.0, agriculture is going through its digital revolution. In fact, Agriculture 4.0 is based on solutions and technologies developed for Industry 4.0. In this context, smart beehives need some technological solutions to build smart hive motoring systems. The goal is to help different stakeholders to make the best decisions. We studied through a spatio-temporal matrix with the time and the geographical horizons all kinds of decisions that can be made. Indeed, beekeeping is affected by a crisis due to the disappearance of bees, and the different stakeholders need support to make their decisions. Our approach used a part of these technologies: Big Data, the Internet of Things, Cyber-Physical Systems, Cloud Computing, and Artificial Intelligence. We have implemented a Digital Transformation (DT) process to help the actors of the beekeeping sector. The idea was to develop new services for beekeepers and more generally for the actors of the apiary using these technologies. Among the elements of the DT, the \"people\" become essential, and even more important than anything else [10]. It is why beekeepers and their cooperatives were fully involved. We used their vocabulary and we worked together to understand their culture. Concretely, a reusable and flexible architecture based on sensors, data from beekeepers, and Open Data has been introduced. To illustrate our new approach, a specialized software application has been developed. This application notifies and alerts beekeepers to help them to take care of their hives. We also proposed a Maturity Model  
Figure 14: System data and information. Figure 15: Example of alerts and notifications from environmental data. (MM) to diagnose the level of digitalization in the area of agriculture. We analysed the literature on smart beehives regarding this MM. A long term perspective is to move toward the development of a methodology to address issues related to Agriculture 4.0 and support agriculture in their DT in general. Another important perspective is to use this approach to build a global architecture. It allows to register all the data at different hierarchical levels (land, apiary, and beehive databases). Such an architecture would be based on Industry 4.0 and Fog computing (Edge Computing). We will analyse architectures proposed by these concepts [49]. Moreover, our use case will evolve toward more local decisions, and will correspond to level 4 of our MM (Table 1). It means that the decisions will be faster, more relevant, efficient, and accurate. Concretely, this evolution will be achieved thanks to the development of a data analysis module inside the beehives. This will allow us to cross-check and analyse the history of the data acquired. Another future line of research is the development of cooperation between the embedded systems of smart beehives of the same apiary. It will generate better accuracy for the notification and alert system. To achieve all these evolutions, we need to implement machine and deep learning algorithms. Conceptualization, J.-C.H., L.B. and Y.K. ; Methodology, L.B. and Y.K. ; Project administration, J.-C.H. ; Software, L.B. and Y.K. ; Supervision, C.A. ; Validation, K.W.-W. and C.A. ; Writing--original draft, J.-C.H., L.B., Y.K. and K.W.-W.; Writing--review & editing, J.-C.H., L.B., Y.K., K.W.-W. and C.A. All authors have read and agreed to the published version of the manuscript. This research was funded partially by French Chambers of Agriculture development programs (CASDAR) and the APC was funded by EFREI Paris (engineering school of digital technologies). Not applicable. The authors gratefully acknowledge the Ministry of Agriculture and Food, who are financing PNAPI through CASDAR (the special appropriation account \"Agriculture and Rural Development\") under the project number 18 ART 1831 as well as the support and help of [PERSON], ITSAP (Technical and Scientific Institute of Beekeeping and Pollination).