Summary
BeeOimpact platform (incl. BeeOmonitoring and BeeOgarden mobile app) is a unique SaaS solution using the bee ecosystem to extract valuable biodiversity data and metrics. Bees forage 4 bn flowers on a circular surface of 80 to 700ha /solitary bee nest/colony. They gather pollen from these flowers and bring them back in the bee nest or beehive. We collect pollen samples daily via a device placed outside the hive or in the bee nest. As pollen samples capture air and part of soil pollutants and contain plants’ DNA, we will analyze this data (we already have 10yrs of aggregated data) to feed the AI platform. BeeOimpact will provide an assessment of a site, with just 1 click, without having to analyse the samples coming from each site, via AI, expert systems, data mining, machine Learning and deep Learning techs, the origin and assess the level of poorly monitored pollutants and plant diversity level/quality. Then targeted solutions to enhance biodiversity and lower pollution are provided
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/190123465 |
| Start date: | 01-03-2023 |
| End date: | 31-12-2024 |
| Total budget - Public funding: | 2 494 584,28 Euro - 1 746 209,00 Euro |
Cordis data
Original description
BeeOimpact platform (incl. BeeOmonitoring and BeeOgarden mobile app) is a unique SaaS solution using the bee ecosystem to extract valuable biodiversity data and metrics. Bees forage 4 bn flowers on a circular surface of 80 to 700ha /solitary bee nest/colony. They gather pollen from these flowers and bring them back in the bee nest or beehive. We collect pollen samples daily via a device placed outside the hive or in the bee nest. As pollen samples capture air and part of soil pollutants and contain plants’ DNA, we will analyze this data (we already have 10yrs of aggregated data) to feed the AI platform. BeeOimpact will provide an assessment of a site, with just 1 click, without having to analyse the samples coming from each site, via AI, expert systems, data mining, machine Learning and deep Learning techs, the origin and assess the level of poorly monitored pollutants and plant diversity level/quality. Then targeted solutions to enhance biodiversity and lower pollution are providedStatus
SIGNEDCall topic
HORIZON-EIC-2022-ACCELERATOROPEN-01Update Date
31-07-2023
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