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

Projektinformationen

Smart Droplets

ID Finanzhilfevereinbarung: 101070496

DOI

10.3030/101070496

Projekt abgeschlossen

EK-Unterschriftsdatum 14 Juni 2022

Startdatum 1 September 2022

Enddatum 28 Februar 2026

Finanziert unter

• Digital, Industry and Space

Gesamtkosten

€ 3 175 475,25

EU-Beitrag

€ 2 998 370,00

Koordiniert durch

GEOPONIKO PANEPISTIMION ATHINON
Greece

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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

Veröffentlichungen

Peer reviewed articles (6)

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

Autoren: Hilmy Baja, Michiel G.J. Kallenberg, Herman N.C. Berghuijs, Ioannis N. Athanasiadis
Veröffentlicht in: Computers and Electronics in Agriculture, Ausgabe 237, 2025, ISSN 0168-1699
Herausgeber: Elsevier BV
DOI: 10.1016/J.COMPAG.2025.110554

Interoperable agricultural digital twins with reinforcement learning intelligence

Autoren: Michiel Kallenberg, Hilmy Baja, Mihailo Ilić, Aleksandar Tomčić, Milenko Tošić, Ioannis Athanasiadis
Veröffentlicht in: Smart Agricultural Technology, Ausgabe 12, 2025, ISSN 2772-3755
Herausgeber: Elsevier BV
DOI: 10.1016/J.ATECH.2025.101412

Causality and Explainability for Trustworthy Integrated Pest Management

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

Domain adaptation with transfer learning for pasture digital twins

Autoren: Pylianidis, C.; Kallenberg, M.G.J.; Athanasiadis, I.N.
Veröffentlicht in: Crossref, 2024, ISSN 2634-4602
Herausgeber: Cambridge University Press
DOI: 10.1017/EDS.2024.6

Nitrogen management with reinforcement learning and crop growth models

Autoren: Michiel G.J. Kallenberg, Hiske Overweg, Ron van Bree and Ioannis N. Athanasiadis
Veröffentlicht in: Environmental Data Science, 2023, ISSN 2634-4602
Herausgeber: 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

Autoren: Hilmy Baja, Michiel Kallenberg, Ioannis N. Athanasiadis
Veröffentlicht in: Proceedings of the AAAI Conference on Artificial Intelligence, Ausgabe 39, 2025, ISSN 2374-3468
Herausgeber: 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

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

Learning Long-Term Crop Management Strategies with CyclesGym

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

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

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

Leveraging Behavior Trees for Hybrid Autonomous Navigation in Seasonal Agricultural Environments

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