From Bees as a Service (BaaS) to Software as a Service (SaaS) to enhance biodiversity and reduce pollution

Informations projet

BeeOimpact

N° de convention de subvention: 190123465

DOI

10.3030/190123465

Projet clôturé

Date de signature de la CE 18 Janvier 2023

Date de début 1 Mars 2023

Date de fin 30 Juin 2025

Financé au titre de

• The European Innovation Council (EIC)

Coût total

€ 2 494 584,28

Contribution de l’UE

€ 1 746 209,00

Coordonné par

BEEODIVERSITY
Belgium

Periodic Reporting for period 2 - BeeOimpact (From Bees as a Service (BaaS) to Software as a Service (SaaS) to enhance biodiversity and reduce pollution)

Période du rapport: 2024-02-01 au 2025-06-30

Résumé du contexte et des objectifs généraux du projet

BeeOimpact, including the BeeOgarden mobile app, is a cutting-edge SaaS solution designed to empower companies and public entities in assessing biodiversity risks and opportunities across their sites or assets. This AI-powered platform leverages recognized datasets, satellite imagery, and over a decade of monitoring data from more than 100 sites to provide comprehensive, site-specific assessments. BeeOimpact not only identifies risks but also recommends actionable strategies to mitigate them, centralizing the management of these actions across multiple sites.
With BeeOimpact, users can prioritize sites based on biodiversity impact, streamline compliance with sustainability regulations, and make informed decisions that enhance ecological and economic returns. Ultimately, BeeOimpact aims to empower everyone to take meaningful action in protecting and regenerating biodiversity, fostering a sustainable future for all.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

• developed a Smart beehive solution, integrating various sensors to monitor additional environmental parameters and improve our suite of ecosystem monitoring solutions
• developed and requested a patent for an innovative computer vision model and imaging hardware to automatically detect pollen, ie flora.
• developed and piloted a mobile app to empower citizens and green space managers to map and improve local biodiversity
• co-developed and launched a B2B ML/AI powered risk assessment and action-oriented platform, correlating our 10 years of biodiversity data with additional datasets/layers, satellite imagery and models.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

Our project has achieved significant advancements that extend beyond current industry standards and practices.
The integration of smart beehive solutions with advanced sensors enables unique monitoring of environmental parameters, providing a more comprehensive understanding of ecosystem dynamics.
The development of a patented (ongoing process) computer vision model and imaging hardware for automatic pollen detection represents a leap forward in flora identification technology. This system enables much faster and efficient biodiversity assessments compared to traditional methods.

Our mobile app empowers citizens and green space managers to actively participate in biodiversity mapping and improvement efforts, fostering community engagement and democratizing environmental stewardship. This tool is a pioneering approach to citizen science, enhancing local biodiversity management through user-friendly technology.
The B2B ML/AI-powered risk assessment platform correlates a decade of biodiversity data with additional datasets, satellite imagery, and models, providing a holistic view of environmental risks and opportunities. This platform not only improves decision-making processes but also offers actionable insights that drive sustainable practices across industries, setting a new standard for data-driven environmental management.

Our key priority is now to scale the commercialization of these solutions and their impact.

Pitch at the final event of the Accelerator100+ in New York

Lab work to process samples that are analysed