BEEPOLL: Developing land vegetation patterns through earth observation and Artificial Intelligence to optimise beekeeping and pollination measures

The vital role of bees as pollinators is a cornerstone of global economic contributions. The economic significance of pollination globally is immense, and is estimated at €153 billion worldwide, constituting 9.5% of the value of agricultural production for human consumption in 2005. Essential for over 70% of food crops and 35% of food production, as well as 84% of vegetables in Europe, pollinators play a crucial role in food systems. 

Addressing this, the BEEPOLL project which will develop online tools for national authorities and land managers to identify optimal honeybee translocation sites, building upon successful prototype testing in Malta. The project tackles multifaceted challenges, including segmenting local vegetation dynamics via remote sensing using Sentinel 2 and Plaedias imagery, and 2D neural networks, to discern floral resources for honeybees and wild bees. Furthermore, it integrates Internet-of-Things (IoT) logging sensors, software, and on-site field surveys to monitor the abundance and diversity of managed and wild bees. Additionally, it combines remote sensing insights with bee analyses to assess pollination demand and supply across seasons. The BEEPOLL project stands poised to enhance pollinator conservation strategies and advance our understanding of these intricate ecosystems.

Objectives, actions and expected results

Combine drone and satellite images for habitat assessment

Combine drone and satellite images to map habitat and floral resources at high resolution

Use IoT to monitor bee health across a landscape gradient

Use Internet of Things (IoT) technology  to evaluate the impact of habitat and intra-/interspecific interactions on honey bee health

Develop GIS cloud with species distribution models

Develop GIS cloud map displaying the species distribution models, interactions between managed and wild bees, and pollination ecosystem service

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BEEPOLL is funded by the Malta Council of Science and Technology Space Research Fund