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Tools and methods for extended plant PHENotyping and EnviroTyping services of European Research Infrastructures

Descrizione del progetto

Servizi basati sull’intelligenza artificiale per la fenotipizzazione agricola

In Europa è necessaria una transizione agroecologica degli ecosistemi agricoli per garantire una produzione alimentare sostenibile, la resilienza ai cambiamenti climatici, la biodiversità e il ripristino del suolo. Il progetto PHENET, finanziato dall’UE, unirà le infrastrutture europee di ricerca sulla fenotipizzazione vegetale (EMPHASIS), sulle sperimentazioni sugli ecosistemi (AnaEE), sull’osservazione a lungo termine (eLTER) e sulla gestione dei dati e la bioinformatica (ELIXIR) per co-sviluppare nuovi strumenti e metodi per l’identificazione di combinazioni di specie, genotipi e prassi di gestione adatte al futuro. Il progetto fornirà una serie di servizi che permettano l’accesso a fonti più ampie di dati fenotipici e ambientali in situ, sfruttando nuovi dispositivi multi-caratteristica e multisensore basati sull’intelligenza artificiale, l’accesso a dati ad alta risoluzione sull’osservazione della Terra, il sostegno di dati FAIR (reperibili, accessibili, interoperabili e riutilizzabili) e una nuova generazione di modellizzazione predittiva con IA e soluzioni basate su gemelli digitali.

Obiettivo

Europe urgently needs to find pathways towards agroecological transition of agroecosystems in support to food security, climate change resilience, biodiversity and soil carbon stocks restoration. In PHENET, the European Research Infrastructures (RI) on plant phenotyping (EMPHASIS), ecosystems experimentation (AnaEE), long-term observation (eLTER) and data management and bioinformatics (ELIXIR) will join their forces to co-develop, with a diversity of innovative companies, new tools and methods - meant to contribute to new RI services - for the identification of future-proofed combinations of species, genotypes and management practices in front of the most likely climatic scenarios across Europe. Ambitioning to go beyond current highly instrumented but often spatially and temporally limited RI installations, PHENET derived services will allow wide access to enlarged sources of in-situ phenotypic and environmental data thanks to (i) new AI-based multi (agroecology-related) traits multi-sensors devices (ii) to unleashed access to high resolution Earth Observation data connected to ground based data, (iii) FAIR data support for connection with (iv) new generation of predictive modeling solutions encompassing AI and digital twins. Developments will be challenged by and implemented in a series of eight Use Cases covering a large range of agroecosystems but also of ecosystems to demonstrate portability of solutions. Several of these Use Cases will mobilize on-farm data. A large effort will be devoted to training RI staff and beyond through a sustained collection of training material fed by experts. Outreaching activities will aim at enlarging the range of RI users. PHENET will not only strengthen RI but will also have major impact on the development of innovative companies on phenotyping, envirotyping and precision agriculture as well as on the emergence of climate smart crop varieties and innovative practices fitted to climate change and agroecological transition.

Coordinatore

INSTITUT NATIONAL DE RECHERCHE POUR L'AGRICULTURE, L'ALIMENTATION ET L'ENVIRONNEMENT
Contribution nette de l'UE
€ 1 783 443,00
Indirizzo
147 RUE DE L'UNIVERSITE
75007 Paris
Francia

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Regione
Ile-de-France Ile-de-France Paris
Tipo di attività
Research Organisations
Collegamenti
Costo totale
€ 1 783 443,75

Partecipanti (27)

Partner (3)