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

Información del proyecto

Smart Droplets

Identificador del acuerdo de subvención: 101070496

DOI

10.3030/101070496

Proyecto cerrado

Fecha de la firma de la CE 14 Junio 2022

Fecha de inicio 1 Septiembre 2022

Fecha de finalización 28 Febrero 2026

Financiado con arreglo a

• Digital, Industry and Space

Coste total

€ 3 175 475,25

Aportación de la UE

€ 2 998 370,00

Coordinado por

GEOPONIKO PANEPISTIMION ATHINON
Greece

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

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

Publicaciones

Peer reviewed articles (4)

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

Autores: Hilmy Baja, Michiel G.J. Kallenberg, Herman N.C. Berghuijs, Ioannis N. Athanasiadis
Publicado en: Computers and Electronics in Agriculture, Edición 237, 2025, ISSN 0168-1699
Editor: Elsevier BV
DOI: 10.1016/J.COMPAG.2025.110554

Interoperable agricultural digital twins with reinforcement learning intelligence

Autores: Michiel Kallenberg, Hilmy Baja, Mihailo Ilić, Aleksandar Tomčić, Milenko Tošić, Ioannis Athanasiadis
Publicado en: Smart Agricultural Technology, Edición 12, 2025, ISSN 2772-3755
Editor: Elsevier BV
DOI: 10.1016/J.ATECH.2025.101412

Nitrogen management with reinforcement learning and crop growth models

Autores: Michiel G.J. Kallenberg, Hiske Overweg, Ron van Bree and Ioannis N. Athanasiadis
Publicado en: Environmental Data Science, 2023, ISSN 2634-4602
Editor: 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

Autores: Hilmy Baja, Michiel Kallenberg, Ioannis N. Athanasiadis
Publicado en: Proceedings of the AAAI Conference on Artificial Intelligence, Edición 39, 2025, ISSN 2374-3468
Editor: 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

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

Learning Long-Term Crop Management Strategies with CyclesGym

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

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

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

Leveraging Behavior Trees for Hybrid Autonomous Navigation in Seasonal Agricultural Environments

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