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CORDIS - Risultati della ricerca dell’UE
CORDIS

Soil Quality Analysis Tool: Implementing Smart Farming Applications using EO Data, Soil Sensors & Robotics

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

D2.6: User manual for System using SQAT Robots (si apre in una nuova finestra)

A technical user manual detailing procedures for field use of both robots used by the SQAT smart soil mapping system. (Leader: EV ILVO, M18/M33, R, PU)

D1.1 Management & coordination plan (si apre in una nuova finestra)

a strategic document defining the management structure and operating procedures, as well as their implementation (including measure, KPIs and milestones). The plan will also contain a categorization of risks, with specific mitigation measures and approaches.

D1.2 Gender equality & ethics management plan (si apre in una nuova finestra)

the document will enclose detailed strategies on how the project will ensure gender equality and proper ethics management in its activities (Lead: ABE, M03, ETHICS, PU)

D2.1: Use case implementation plans (si apre in una nuova finestra)

A document detailing specific activities across a timeline for deployment of the use cases. It will also define the evaluation methodology to be used (Leader: ABE, M03, R, PU)

D4.2: Data governance principles: (si apre in una nuova finestra)

A report providing a tailored solution of data goverance principles for SQATand a set of measures to apply them to use cases (Leader: FarmEye, M06, Report, PU)

D5.1: Exploitation, dissemination, and communication strategy (si apre in una nuova finestra)

A document detailing the project’s strategic approach to exploitation, dissemination, and communication activities, on the basis of KPI-driven objectives, target and audience differentiation (Leader: ABE, M03, Report, PU)

D4.1: Data Management Plan (si apre in una nuova finestra)

A data management plan document in line with the FAIR principles to promote project data to be findable, accessible, interoperable and reusable (Leader: FarmEye, M08, DMP, PU)

D5.2: Communication kit and project website (si apre in una nuova finestra)

A package of materials for partners to use to promote the project and the project website. (Leader: ABE, M03, DEC, PU)

D2.5: Lightweight and heavy-duty sampler device with proximity sensors (si apre in una nuova finestra)

An electro-mechanical arm attachable to robotic platforms for soil sampling in different soil conditions. (Leader: ILT-OST, M18, OTHER, PU)

Pubblicazioni

Overfitting due to data leakage in soil sensor calibration: Examples from lab-based and in-situ soil NIR spectroscopy (si apre in una nuova finestra)

Autori: José Correa, Hamed Tavakoli, Sebastian Vogel, Robin Gebbers
Pubblicato in: Computers and Electronics in Agriculture, Numero 239, 2025, ISSN 0168-1699
Editore: Elsevier BV
DOI: 10.1016/J.COMPAG.2025.110920

Multi-Source Data Integration for Sustainable Management Zone Delineation in Precision Agriculture (si apre in una nuova finestra)

Autori: Dušan Jovanović, Miro Govedarica, Milan Gavrilović, Ranko Čabilovski, Tamme van der Wal
Pubblicato in: Sustainability, Numero 17, 2025, ISSN 2071-1050
Editore: MDPI AG
DOI: 10.3390/SU17156931

Development and In-Field Validation of an Autonomous Soil Mechanical Resistance Sensor (si apre in una nuova finestra)

Autori: Valentijn De Cauwer, Simon Cool, Axel Willekens, Sébastien Temmerman, David Nuyttens, Tommy D’ Hose, Jan Pieters, Sam Leroux
Pubblicato in: Sensors, Numero 25, 2025, ISSN 1424-8220
Editore: MDPI AG
DOI: 10.3390/S25061919

Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors (si apre in una nuova finestra)

Autori: J. Schmidinger, V. Barkov, H. Tavakoli, J. Correa, M. Ostermann, M. Atzmueller, R. Gebbers, S. Vogel
Pubblicato in: Geoderma, Numero 450, 2024, ISSN 0016-7061
Editore: Elsevier BV
DOI: 10.1016/J.GEODERMA.2024.117017

Effect of training sample size, sampling design and prediction model on soil mapping with proximal sensing data for precision liming (si apre in una nuova finestra)

Autori: Jonas Schmidinger, Ingmar Schröter, Eric Bönecke, Robin Gebbers, Joerg Ruehlmann, Eckart Kramer, Vera L. Mulder, Gerard B. M. Heuvelink, Sebastian Vogel
Pubblicato in: Precision Agriculture, Numero 25, 2024, ISSN 1385-2256
Editore: Springer Science and Business Media LLC
DOI: 10.1007/S11119-024-10122-3

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