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Living Hubs in the INNO4CFIs Project. From Ground to Satellites to AI Applications: Integrating Advanced Technologies for Carbon Farming in Agroforestry

  • Niccolò Conti
  • , Lorenzo Scatena
  • , Eleonora Lombardi
  • , Valerio Roscani
  • , Elisabetta Meconcelli
  • , Federico Franciamore
  • , Diego Valdeolmillos
  • , Elena Marra
  • , Jacopo Manzini
  • , Pierluigi Paris
  • , Lorenzo Costanzo
  • , Alessandro Villa
  • , Pinelopi Papadopoulou
  • , Andriani Galani
  • , Enrique Olea Alonso
  • , José Belenguer Ballester
  • , Gianni Della Rocca*
  • *Huvudförfattare för detta arbete

Publikation: Kapitel i bok/rapport/konferenshandlingKonferensartikel i proceedingsPeer review

Sammanfattning

In the global effort to reduce greenhouse gas emissions, innovative carbon farming activities could play a prominent role in the sequestration of atmospheric CO2. By implementing state-of-the-art technological solutions, the EU-funded INNO4CFIs project will assess C tree sequestration in four European agroforestry Living Hubs (LHs). For sustainable freshwater production in agroforestry systems, the performance of an IoT-supported desalination system (Mangrove Technology Platform, MTP) will be validated in the reference LH of Follonica (Grosseto, Italy). As an indicator of C sequestration, tree biomass accumulation will be monitored using a cutting-edge approach that integrates traditional tree allometric equations to satellite- and UAV drone-based remote sensing analyses. Ecophysiological measurements by sensors will characterize tree performances and functionality. All the physiological, agronomic, and carbon accumulation data from the different agroforestry plantations will be directed to a decentralized data management that guarantees security measures and data traceability for end-users and stakeholders. Finally, advanced Artificial Intelligence (AI) methodologies will support the project in developing a peer-to-peer (P2P) carbon credit recommendation engine. Overall, INNO4CFIs will offer an advanced and unique analysis of AFS potential as a carbon offset strategy.

OriginalspråkEngelska
Titel på värdpublikationDistributed Computing and Artificial Intelligence, Special Sessions I, 21st International Conference
RedaktörerRashid Mehmood, Guillermo Hernández, Isabel Praça, Jaroslaw Wikarek, Roussanka Loukanova, Arsénio Monteiro dos Reis, Antonio Skarmeta, Eleonora Lombardi
FörlagSpringer Science and Business Media Deutschland GmbH
Sidor399-408
Antal sidor10
ISBN (tryckt)9783031764585
DOI
StatusPublicerad - 2025
Externt publiceradJa
Evenemang21st International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024 - Salamanca, Spanien
Varaktighet: 25 juni 202427 juni 2024

Publikationsserier

SerieLecture Notes in Networks and Systems
Volym1198 LNNS
ISSN2367-3370

Konferens

Konferens21st International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024
Land/TerritoriumSpanien
OrtSalamanca
Period2024-06-252024-06-27

Bibliografisk information

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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