Accelerating the achievement of EU Green Deal Goals for pesticide and fertilizer reduction through AI, data and robotic technologies.

Informacje na temat projektu

Smart Droplets

Identyfikator umowy o grant: 101070496

DOI

10.3030/101070496

Projekt został zamknięty

Data podpisania przez KE 14 Czerwca 2022

Data rozpoczęcia 1 Września 2022

Data zakończenia 28 Lutego 2026

Finansowanie w ramach

• Digital, Industry and Space

Koszt całkowity

€ 3 175 475,25

Wkład UE

€ 2 998 370,00

Koordynowany przez

GEOPONIKO PANEPISTIMION ATHINON
Greece

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Rezultaty

Documents, reports (7)

Dissemination, communication and exploitation plan and reports v2

The main reference for communication activities with guidelines for partners to disseminate and exploit results Also includes an action plan on the planned liaisons with complementary AI Data and Robotics initiatives

Dissemination, communication and exploitation plan and reports v3

The main reference for communication activities, with guidelines for partners to disseminate and exploit results. Also includes an action plan on the planned liaisons with complementary AI, Data and Robotics initiatives.

Dissemination, communication and exploitation plan and reports v1

The main reference for communication activities with guidelines for partners to disseminate and exploit results Also includes an action plan on the planned liaisons with complementary AI Data and Robotics initiatives

Smart Droplets Academy - Program, Timeline & Activities v1

Work plan on training activities includes the production of learning kits and recorded video content

Reference Architectures and Resources Mapping

Report on reference architectures, common resources and prospective links with other AI, Data and Robotics projects and initiatives

Industry Needs & Requirements Analysis v2

Assessment report on industry needs, incl. mapping of requirements and metrics/ KPIs that relate to the use case implementation. Output of T2.1, T2.2.

Industry Needs & Requirements Analysis v1

Assessment report on industry needs, incl. mapping of requirements and metrics/ KPIs that relate to the use case implementation. Output of T2.1, T2.2

Publikacje

Peer reviewed articles (6)

Adaptive fertilizer management for optimizing nitrogen use efficiency with constrained reinforcement learning

Autorzy: Hilmy Baja, Michiel G.J. Kallenberg, Herman N.C. Berghuijs, Ioannis N. Athanasiadis
Opublikowane w: Computers and Electronics in Agriculture, Numer 237, 2025, ISSN 0168-1699
Wydawca: Elsevier BV
DOI: 10.1016/J.COMPAG.2025.110554

Interoperable agricultural digital twins with reinforcement learning intelligence

Autorzy: Michiel Kallenberg, Hilmy Baja, Mihailo Ilić, Aleksandar Tomčić, Milenko Tošić, Ioannis Athanasiadis
Opublikowane w: Smart Agricultural Technology, Numer 12, 2025, ISSN 2772-3755
Wydawca: Elsevier BV
DOI: 10.1016/J.ATECH.2025.101412

Causality and Explainability for Trustworthy Integrated Pest Management

Autorzy: Ilias Tsoumas, Vasileios Sitokonstantinou, Georgios Giannarakis, Evagelia Lampiri, Christos Athanassiou, Gustau Camps-Valls, Charalampos Kontoes, Ioannis Athanasiadis
Opublikowane w: Workshop on Tackling Climate Change with Machine Learning, NeurIPS2023, 2023, ISSN 1049-5258
Wydawca: Cornell University / arXiv.
DOI: 10.48550/ARXIV.2312.04343

Domain adaptation with transfer learning for pasture digital twins

Autorzy: Pylianidis, C.; Kallenberg, M.G.J.; Athanasiadis, I.N.
Opublikowane w: Crossref, 2024, ISSN 2634-4602
Wydawca: Cambridge University Press
DOI: 10.1017/EDS.2024.6

Nitrogen management with reinforcement learning and crop growth models

Autorzy: Michiel G.J. Kallenberg, Hiske Overweg, Ron van Bree and Ioannis N. Athanasiadis
Opublikowane w: Environmental Data Science, 2023, ISSN 2634-4602
Wydawca: Cambridge University Press.
DOI: 10.1017/eds.2023.28

To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning

Autorzy: Hilmy Baja, Michiel Kallenberg, Ioannis N. Athanasiadis
Opublikowane w: Proceedings of the AAAI Conference on Artificial Intelligence, Numer 39, 2025, ISSN 2374-3468
Wydawca: Association for the Advancement of Artificial Intelligence (AAAI)
DOI: 10.1609/AAAI.V39I27.34999

Conference proceedings (4)

Integrating processed-based models and machine learning for crop yield prediction

Autorzy: Michiel G.J. Kallenberg, Bernardo Maestrini, Ron van Bree, Paul Ravensbergen, Christos Pylianidis, Frits van Evert, Ioannis N. Athanasiadis
Opublikowane w: Workshop on Synergy of Scientific and Machine Learning Modeling, International Conference on Machine Learning, 2023, ISSN 2331-8422
Wydawca: ArXiv.org
DOI: 10.48550/arXiv.2307.13466

Learning Long-Term Crop Management Strategies with CyclesGym

Autorzy: Turchetta, Matteo; Corinzia, Luca; Sussex, Scott; Burton, Amanda; Herrera, Juan; Athanasiadis, Ioannis N.; Buhmann, Joachim M.; Krause, Andreas
Opublikowane w: ISBN: 9781713871088, 2022

Interoperability as an Enabler for Digital Twin Based Decision Making in Smart Agriculture

Autorzy: M. Ilić; M. Tošić; A. Tomčić
Opublikowane w: 2025 48th ICT and Electronics Convention (MIPRO), 2025
Wydawca: IEEE (in cooperation with MIPRO — Croatian Society MIPRO, Rijeka/Opatija, Croatia)

Leveraging Behavior Trees for Hybrid Autonomous Navigation in Seasonal Agricultural Environments

Autorzy: Juan Francisco Rascón, Pau Reverté, Xavier Ruiz, Mateus S. Moura, Daniel Serrano, Carlos Rizzo
Opublikowane w: 2024 7th Iberian Robotics Conference (ROBOT), 2024
Wydawca: IEEE
DOI: 10.1109/ROBOT61475.2024.10797423