# Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
## 5 Decision Support Systems for Smart Beehives
### Databases  
Our system is able to send valuable information to subscribed beekeepers thanks to the collection and extraction of data from different data sources such as sensors. The collected data will help train machine learning models capable of generating advice or alerts to users. A survey was carried out by our partner (ITSAP--Bee Institute). The survey is based on multiple questionnaires on the several activities and needs of beekeepers (Survey \"Beekeepers and digital technology\". [[https://itsqap.asso.fr/pages_thematiques/numerique/enquette-apicitleurs-numerique/](https://itsqap.asso.fr/pages_thematiques/numerique/enquette-apicitleurs-numerique/)]([https://itsqap.asso.fr/pages_thematiques/numerique/enquette-apicitleurs-numerique/](https://itsqap.asso.fr/pages_thematiques/numerique/enquette-apicitleurs-numerique/)), 31 January 2020. accessed on 28 September 2022). The number of beekeepers participating in the survey is 415 (43% have more than 70 hives).The most important productions are honey and breeding. According to the survey, data collection appears to be a relatively common practice among beekeepers. While certain information must necessarily be collected to meet administrative requirements (especially for the breeding register), others show a desire to collect objective information on the colonies. However, a handwritten input remains the majority in most cases, despite the advantage of having a computer version concerning the ease of consultation of the data and their use. Only 60% of beekeepers who responded to the survey monitor varroa infestation. The most expected features consist of fairly simple data processing, namely graphic productions and indicator tables. In addition to data visualization, beekeepers are looking for an intelligent alert service capable of reporting relevant information (Table 3). Figure 5: Detected bees by Faster RCNN. Coloured bounding boxes are predicted objects and white bounding boxes are ground-truth objects. Figure 6: Varroa segmentation by U-NET. The yellow region represents varroa. True Mask is ground-truth segmentation and Predicted Mask is the predicted segmentation. The proposed data model is based on the results of this survey concerning the useful and necessary information that should be managed on an apiary. The implementation of this architecture is done in collaboration with beekeepers. This will lead to the development of a comprehensive and flexible system that would allow the beekeeping community to share relevant information and knowledge, and make appropriate decisions. All information is modelled from data models that allow the unification of data structures using standards and also ensure further extensibility, if necessary. Figure 7 presents an excerpt from our data model. We have developed a new model that would meet the needs of our beekeepers. Conceptually, the model presented is based on two levels:  
* Spatial dimensions: sensors/hive/apiary/zone;
* Temporal dimensions: real-time /operational(day /week)/tactic (week/month) /strategic (year). \begin{table}
\begin{tabular}{c c} \hline \hline
**Functionality** & **Interested Beekeepers (\%)** \\ \hline Data visualization & 81 \\ \hline Alert service & 78 \\ \hline Voice input & 77 \\ \hline Data storage & 72 \\ \hline Data sharing between beekeepers & 71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Expectations of beekeepers regarding the functionalities to be integrated into a management platform. Figure 7: Excerpt from the data model. Figure 8 presents an excerpt from our central data mode. The figure shows an example of a service offered, collecting data by integrating the time dimension. As an example, for an apiary:  
Real-time: quantity of honey;  
Operational: varroa counting;  
Tactic: varroa treatment;  
Strategic: define the type and the amount of treatment and write a health risk management. The selection of materials used to build the embedded system is a compromise between the cost and the accuracy of the sensors.