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  Model-as-a-Service for Sustainable Viticulture: A Comprehensive Science-Driven Decision Support Platform

Adão, T., Pascoal, D., Portela, F., Pádua, L., Fernandes, A., Fonseca, A., Freitas, T., Fraga, H., Menz, C., Chojka, A., Silva, N., Santos, J., Peres, E., Morais, R. (2026): Model-as-a-Service for Sustainable Viticulture: A Comprehensive Science-Driven Decision Support Platform. - Procedia Computer Science, 278, 340-351.
https://doi.org/10.1016/j.procs.2026.02.471

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 Creators:
Adão, Telmo1, Author
Pascoal, David1, Author
Portela, Fernando1, Author
Pádua, Luís1, Author
Fernandes, António1, Author
Fonseca, André1, Author
Freitas, Teresa1, Author
Fraga, Hélder1, Author
Menz, Christoph2, Author                 
Chojka, Agnieszka1, Author
Silva, Nuno1, Author
Santos, João1, Author
Peres, Emanuel1, Author
Morais, Raul1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The scientific knowledge in viticulture produced by academic and research institutions is vast but often remains confined to those contexts. In an era of digital transformation and increasing connectivity, it is imperative to make this knowledge accessible to those who can benefit the most: agribusiness service providers and viticulture professionals. With this need in mind, this article proposes the development of a Model-as-a-Service (MaaS) platform designed to deliver consolidated scientific knowledge as consumable digital services. By integrating an operational Internet of Things (IoT) sensor network oriented to precision agriculture/viticulture – known as mySense – along with meteorological-based data sources, the platform offers grapevine-specific decision support by enabling the forecasting of irrigation requirements, tracking phenological stages, and predicting pest and disease outbreaks. The platform also supports climatic projections and remote sensing-based monitoring. Although still under development, several MaaS services are already implemented and available, as detailed in this article.

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Language(s): eng - English
 Dates: 2026-03-242026-03-24
 Publication Status: Finally published
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.procs.2026.02.471
MDB-ID: No data to archive
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Hydroclimatic Risks
Research topic keyword: Food & Agriculture
Model / method: Decision Theory
Model / method: ISIMIP
Model / method: Transfer (Knowledge&Technology)
OATYPE: Gold Open Access
 Degree: -

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Title: Procedia Computer Science
Source Genre: Journal, Scopus, oa
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Pages: - Volume / Issue: 278 Sequence Number: - Start / End Page: 340 - 351 Identifier: Publisher: Elsevier
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1701261