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hivepoll

Financiado

Polling beehives to monitor parasitic infestations and gather pollination inform...

Polling beehives to monitor parasitic infestations and gather pollination information One out of every three bites of food we eat is there because of pollinators. However, pollinator and especially bee diversity has declined markedly in Europe and a study from 2014 by the university of Reading calculates the existi... ver más

30/11/2019

COGVIS GMBH

71K€

Presupuesto del proyecto: 71K€

Líder del proyecto

COGVIS SOFTWARE UND CONSULTING GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-11-30

EIC-SMEInst-2018-2020: SME instrument

I+D

Cerrada hace 6 años

0% 70% 100%

2 Participantes

COGVIS GMBH

50.00K€ | Lider

PLASTICOS DUREX

365.10K€ | Participante

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Duración del proyecto: 6 meses Fecha Inicio: 2019-05-11
Fecha Fin: 2019-11-30

Líder del proyecto

COGVIS SOFTWARE UND CONSULTING GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 71K€

Descripción del proyecto One out of every three bites of food we eat is there because of pollinators. However, pollinator and especially bee diversity has declined markedly in Europe and a study from 2014 by the university of Reading calculates the existing shortage at seven billion bees. In addition to the challenges from climate change and an ever growing world population the agricultural sector is under tremendous pressure to produce a lot more crop on limited acres and a declining pollinator population. Hivepoll aims to establish a novel data exchange platform between beekeepers and other stakeholder in the agriculture market. The project will not only offer a low-cost visual computing system to detect threats and effectively manage bee hives in real time but also unprecedented insights into the performance of bee colonies as pollinators. Hivepoll allows to predict crop yield and using better models to manage crop failure risks, two features which are among the biggest opportunities for Big Data applications in agriculture. With hivepoll it will become possible to provide an early warning system in case of a low pollination performance as well as being able to counter these adverse effects. Furthermore, an effective parasite detection will contribute to the health of bee colonies and their survival in good health over the winter which is a prime concern of bee keepers and farmers as honey bees are among the few pollinators which are active in sufficient force early in the planting cycle. A fast pollination of crops is extremely important as it alleviates the negative effects of unstable weather conditions such as spring frost. We predict an up to 10% increase in crop yield due to the improved management of pollination by hivepoll. Hivepoll is based on a successfully concluded 2 years research project with universities. In the project important follow-up steps for commercialization will be carried out.

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