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

Informations projet

Smart Droplets

N° de convention de subvention: 101070496

DOI

10.3030/101070496

Projet clôturé

Date de signature de la CE 14 Juin 2022

Date de début 1 Septembre 2022

Date de fin 28 Février 2026

Financé au titre de

• Digital, Industry and Space

Coût total

€ 3 175 475,25

Contribution de l’UE

€ 2 998 370,00

Coordonné par

GEOPONIKO PANEPISTIMION ATHINON
Greece

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

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

Publications

Peer reviewed articles (4)

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

Auteurs: Hilmy Baja, Michiel G.J. Kallenberg, Herman N.C. Berghuijs, Ioannis N. Athanasiadis
Publié dans: Computers and Electronics in Agriculture, Numéro 237, 2025, ISSN 0168-1699
Éditeur: Elsevier BV
DOI: 10.1016/J.COMPAG.2025.110554

Interoperable agricultural digital twins with reinforcement learning intelligence

Auteurs: Michiel Kallenberg, Hilmy Baja, Mihailo Ilić, Aleksandar Tomčić, Milenko Tošić, Ioannis Athanasiadis
Publié dans: Smart Agricultural Technology, Numéro 12, 2025, ISSN 2772-3755
Éditeur: Elsevier BV
DOI: 10.1016/J.ATECH.2025.101412

Nitrogen management with reinforcement learning and crop growth models

Auteurs: Michiel G.J. Kallenberg, Hiske Overweg, Ron van Bree and Ioannis N. Athanasiadis
Publié dans: Environmental Data Science, 2023, ISSN 2634-4602
Éditeur: 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

Auteurs: Hilmy Baja, Michiel Kallenberg, Ioannis N. Athanasiadis
Publié dans: Proceedings of the AAAI Conference on Artificial Intelligence, Numéro 39, 2025, ISSN 2374-3468
Éditeur: 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

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

Learning Long-Term Crop Management Strategies with CyclesGym

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

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

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

Leveraging Behavior Trees for Hybrid Autonomous Navigation in Seasonal Agricultural Environments

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