SAiBees

Objectives of the service

Due to increasingly frequent phenomena such as climate change, landscape fragmentation, exposure to pesticides, the spread of diseases and invasive species, beekeeping is also increasingly subject to pressures linked to environmental factors. Beekeepers often lack adequate spatial information to manage their colonies effectively and make the best use of available natural resources.

The project aims to develop a prototype service that combines satellite monitoring with data-driven models to better understand the relationship between territorial dynamics and conditions suitable for beekeeping. This will result in information products to aid decision-making, including maps, time series and summary indicators, made available to beekeepers to support apiary management and improve the planning of honey-producing resource use. Through the involvement of users and stakeholders, the project will assess the relevance, usability and potential benefits of the service, establishing a reference framework for future operational applications in support of sustainable beekeeping.

Users and their needs

SAiBees targets professional and hobbyist beekeepers, beekeeper associations, and organisations involved in environmental management. These users strongly depend on environmental conditions, which influence nectar resource availability, colony health, and honey production. However, information on landscape dynamics and environmental pressures is often fragmented or difficult to access in a practical and operational form.

The project therefore involves direct engagement with beekeepers and sector stakeholders to ensure that the service addresses real operational needs and can be integrated into existing beekeeping practices.

The main user needs identified include:

Access to timely and spatially detailed information on the availability of nectar sources;
Better understanding of how environmental and climatic conditions influence colony health and productivity;
Decision-support tools to guide hive placement and the seasonal management of beekeeping activities;
Early identification of environmental stress factors that may negatively affect bee colonies;
Simple and accessible information products that can be used in the daily activities of beekeepers.

The target users of the service are in Italy, where the service will be tested and validated through the involvement of local beekeepers and sector organisations.

Service/ system concept

SAiBees provides beekeepers with environmental information to support hive management, derived from the analysis of satellite data, high-resolution aerial imagery and climatic variables. Through automated data processing pipelines and artificial intelligence models, the system generates indicators related to vegetation health, nectar resource availability, and habitat quality in areas surrounding apiaries. These results are provided through a web interface that allows users to view maps, environmental indicators, and spatial analyses to support decisions related to colony management and hive placement.

From a technical standpoint, the system is based on a modular microservices architecture. Data from Earth observation programmes, aerial imagery, and climate datasets are collected and stored in a central repository. Various analysis modules operate on this data, performing tasks such as monitoring vegetation health, phenological analysis, land cover classification and apiary location identification.

The results of these analyses are made available through application services and displayed on the web platform, allowing users to easily access information and explore data through user-friendly maps and visualisation tools.

Space Added Value

SAiBees primarily uses Earth observation satellite data together with climatic data derived from meteorological models. These data are combined with automated analysis models to generate environmental indicators supporting beekeeping management activities. Within the project, the use of satellite data enables the systematic and repeated monitoring of large geographic areas, providing up-to-date information on land use classes, vegetation health conditions, seasonal dynamics, and the availability of nectar resources. This approach offers a comprehensive overview of the landscape and its ecological resources that would be difficult to obtain through local observations, field surveys, or personal knowledge of the territory alone.

Compared to traditional methods, which mainly rely on the direct experience of beekeepers or on field observations, the integration of satellite data and analytical models makes it possible to provide objective, scalable, and regularly updated information. This supports more informed decision making in hive management and in the planning of beekeeping activities, ultimately contributing to improved sustainability and efficiency in the sector.

Furthermore, the use of GNSS technology will enable the precise geolocation of beehives, allowing satellite data to be integrated with the actual location of apiaries and providing spatially explicit analyses that can be directly applied to the operational management and planning of beekeeping activities.

Current Status

At present, user requirements have been captured through stakeholder engagement and are currently being processed and analysed in order to define the service requirements more precisely. A preliminary version of the business plan has been developed, while work continues on all aspects of the Proof of Concept, covering technical, operational and commercial domains.

During the first months of the SAiBees project, several technical and service validation activities have been completed. The desirability assessment and value proposition were finalised and shared within the Consortium. The service was also presented at the AAPI, APIMELL and NextAlpine fair, where feedback was collected from stakeholders to assess interest in the service modules and support the commercial strategy.

From a technical perspective, data collection and preparation have been completed. Initial models were trained using a multi-seasonal pan-European dataset, phenological data supported a first flowering model, and preliminary apiary detection activities were tested using VHR imagery.

The next stages will focus on finalising the business plan, consolidating the models developed, and validating and evaluating the performance of the Proof of Concept, including model accuracy, service reliability and usability. These activities will lead to the conclusion of the feasibility paving the way for the future development of the service and its potential expansion.