Summary
iPollinate will bring to market a unique IoT sensor that provides real time monitoring of the foraging rate of honeybee
colonies used for pollination services. This data will be correlated with hive strength and health data using Machine Learning (ML) techniques to provide a precision pollination service enabling farmers to optimise pollination efficiency and crop yields. Our aim is to
enable sustainable increases in crop yields of 30%. Our commercial objectives are to build a global business providing
pollination intelligence services generating annual sales of over €10million and create 17 high-level jobs in IoT and data
analytics within 5 years. Actions include:
• Advanced design and testing of optoelectronic sensor to collect bee foraging data
• Using ML techniques to generate new metrics for pollination effectiveness
• Creating a Spatial Decision Support System (SDSS) to support pollination management
• Conducting field trials to demonstrate performance
• Implementing structured dissemination and exploitation plan to accelerate market adoption.
We have an industrial consortium of 3 SMEs with complementary expertise:
• IRIDEON (Spain): IoT applications, electronics and software development, prototyping and manufacture
• CANETIS (Italy): Development of tools and systems for analysing bee health and behaviour
• BEEHERO (Israel): Precision pollination services for farmers
The project will accelerate development and adoption of a disruptive innovation and business model for pollination services
enabled by unique IoT sensor technology and advanced data analytics. It also addresses priority Societal Challenges,
specifically relating to Food Security and Sustainable Agriculture. iPollinate will enable increases in crop yield, quality and
nutritional value through more effective pollination, without the input of additional agricultural resources. It also supports the
competitiveness of the European Precision Agriculture sector in a rapidly growing global market.
colonies used for pollination services. This data will be correlated with hive strength and health data using Machine Learning (ML) techniques to provide a precision pollination service enabling farmers to optimise pollination efficiency and crop yields. Our aim is to
enable sustainable increases in crop yields of 30%. Our commercial objectives are to build a global business providing
pollination intelligence services generating annual sales of over €10million and create 17 high-level jobs in IoT and data
analytics within 5 years. Actions include:
• Advanced design and testing of optoelectronic sensor to collect bee foraging data
• Using ML techniques to generate new metrics for pollination effectiveness
• Creating a Spatial Decision Support System (SDSS) to support pollination management
• Conducting field trials to demonstrate performance
• Implementing structured dissemination and exploitation plan to accelerate market adoption.
We have an industrial consortium of 3 SMEs with complementary expertise:
• IRIDEON (Spain): IoT applications, electronics and software development, prototyping and manufacture
• CANETIS (Italy): Development of tools and systems for analysing bee health and behaviour
• BEEHERO (Israel): Precision pollination services for farmers
The project will accelerate development and adoption of a disruptive innovation and business model for pollination services
enabled by unique IoT sensor technology and advanced data analytics. It also addresses priority Societal Challenges,
specifically relating to Food Security and Sustainable Agriculture. iPollinate will enable increases in crop yield, quality and
nutritional value through more effective pollination, without the input of additional agricultural resources. It also supports the
competitiveness of the European Precision Agriculture sector in a rapidly growing global market.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/958827 |
| Start date: | 01-01-2021 |
| End date: | 31-12-2023 |
| Total budget - Public funding: | 2 728 125,00 Euro - 1 909 687,00 Euro |
Cordis data
Original description
iPollinate will bring to market a unique IoT sensor that provides real time monitoring of the foraging rate of honeybeecolonies used for pollination services. This data will be correlated with hive strength and health data using Machine Learning (ML) techniques to provide a precision pollination service enabling farmers to optimise pollination efficiency and crop yields. Our aim is to
enable sustainable increases in crop yields of 30%. Our commercial objectives are to build a global business providing
pollination intelligence services generating annual sales of over €10million and create 17 high-level jobs in IoT and data
analytics within 5 years. Actions include:
• Advanced design and testing of optoelectronic sensor to collect bee foraging data
• Using ML techniques to generate new metrics for pollination effectiveness
• Creating a Spatial Decision Support System (SDSS) to support pollination management
• Conducting field trials to demonstrate performance
• Implementing structured dissemination and exploitation plan to accelerate market adoption.
We have an industrial consortium of 3 SMEs with complementary expertise:
• IRIDEON (Spain): IoT applications, electronics and software development, prototyping and manufacture
• CANETIS (Italy): Development of tools and systems for analysing bee health and behaviour
• BEEHERO (Israel): Precision pollination services for farmers
The project will accelerate development and adoption of a disruptive innovation and business model for pollination services
enabled by unique IoT sensor technology and advanced data analytics. It also addresses priority Societal Challenges,
specifically relating to Food Security and Sustainable Agriculture. iPollinate will enable increases in crop yield, quality and
nutritional value through more effective pollination, without the input of additional agricultural resources. It also supports the
competitiveness of the European Precision Agriculture sector in a rapidly growing global market.
Status
SIGNEDCall topic
EIC-FTI-2018-2020Update Date
26-10-2022
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