Summary
Cultivation of plants consumes huge resources of water for irrigation and energy for lighting. Introducing disruptive technologies is key to improve plants and enable their parsimonious cultivation.
DREAM overtakes these challenges by gathering an interdisciplinary consortium sharing a same radical vision: to measure and exploit the dynamics of photosynthesis regulation under natural-like light conditions for selective sensing – monitoring the plant state – and enhancing lighting efficiency in controlled environments.
We develop ground-breaking instruments and acquisition protocols unraveling unprecedented kinetic data from microalgae and plants (including tomato as a crop species) by using novel periodically modulated or randomly fluctuating illuminations, chlorophyll fluorescence, and original luminescent nanosensors. These data are processed with comprehensive theoretical tools (system identification, dynamical systems, system control, machine learning) to build a powerful dynamical model which delivers categorizing fingerprints for highly selective sensing and tailored modulated illuminations for enhancing lighting efficiency. Finally, we implement a server with incremental learning from an open source community to extend sensing to organisms and environmental conditions much beyond the ones used in our DREAM project.
DREAM will expand knowledge on photosynthesis regulation and lead to major achievements: categorizing plant states (sensing stresses, selecting improved plants), improving controlled ecosystems (equipping lighting with sensing and decreasing its cost), and instrument design (targeting scientists and many more end-users). DREAM will further improve innovation in key European industries active in the fields of scientific instruments, phenotyping, and plant production while increasing resource use efficiency so as to improve environmental quality and offer better and safer products to consumers.
DREAM overtakes these challenges by gathering an interdisciplinary consortium sharing a same radical vision: to measure and exploit the dynamics of photosynthesis regulation under natural-like light conditions for selective sensing – monitoring the plant state – and enhancing lighting efficiency in controlled environments.
We develop ground-breaking instruments and acquisition protocols unraveling unprecedented kinetic data from microalgae and plants (including tomato as a crop species) by using novel periodically modulated or randomly fluctuating illuminations, chlorophyll fluorescence, and original luminescent nanosensors. These data are processed with comprehensive theoretical tools (system identification, dynamical systems, system control, machine learning) to build a powerful dynamical model which delivers categorizing fingerprints for highly selective sensing and tailored modulated illuminations for enhancing lighting efficiency. Finally, we implement a server with incremental learning from an open source community to extend sensing to organisms and environmental conditions much beyond the ones used in our DREAM project.
DREAM will expand knowledge on photosynthesis regulation and lead to major achievements: categorizing plant states (sensing stresses, selecting improved plants), improving controlled ecosystems (equipping lighting with sensing and decreasing its cost), and instrument design (targeting scientists and many more end-users). DREAM will further improve innovation in key European industries active in the fields of scientific instruments, phenotyping, and plant production while increasing resource use efficiency so as to improve environmental quality and offer better and safer products to consumers.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101046451 |
| Start date: | 01-04-2022 |
| End date: | 31-03-2026 |
| Total budget - Public funding: | 3 090 026,35 Euro - 3 090 026,00 Euro |
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Original description
Cultivation of plants consumes huge resources of water for irrigation and energy for lighting. Introducing disruptive technologies is key to improve plants and enable their parsimonious cultivation.DREAM overtakes these challenges by gathering an interdisciplinary consortium sharing a same radical vision: to measure and exploit the dynamics of photosynthesis regulation under natural-like light conditions for selective sensing – monitoring the plant state – and enhancing lighting efficiency in controlled environments.
We develop ground-breaking instruments and acquisition protocols unraveling unprecedented kinetic data from microalgae and plants (including tomato as a crop species) by using novel periodically modulated or randomly fluctuating illuminations, chlorophyll fluorescence, and original luminescent nanosensors. These data are processed with comprehensive theoretical tools (system identification, dynamical systems, system control, machine learning) to build a powerful dynamical model which delivers categorizing fingerprints for highly selective sensing and tailored modulated illuminations for enhancing lighting efficiency. Finally, we implement a server with incremental learning from an open source community to extend sensing to organisms and environmental conditions much beyond the ones used in our DREAM project.
DREAM will expand knowledge on photosynthesis regulation and lead to major achievements: categorizing plant states (sensing stresses, selecting improved plants), improving controlled ecosystems (equipping lighting with sensing and decreasing its cost), and instrument design (targeting scientists and many more end-users). DREAM will further improve innovation in key European industries active in the fields of scientific instruments, phenotyping, and plant production while increasing resource use efficiency so as to improve environmental quality and offer better and safer products to consumers.
Status
SIGNEDCall topic
HORIZON-EIC-2021-PATHFINDEROPEN-01-01Update Date
09-02-2023
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