AgData — EU Partnership on Agriculture of Data

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UC 85 — PlagueAlarm

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Key Information
Title
ntegration in the Agrifood Sector Federated Data Space of Andalusia: Upscaling the Plant Disease Monitoring Use Case to the European Space
Acronym
PlagueAlarm
Coordinator
University of Malaga (Spain)University
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Health, Pests, Diseases & Protection
Sectors
FruitsOther
Data Types
Machine sensor dataEarth observation dataFarm Management Information Systems (FMIS) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.1 Data acquisition & re-use framework
  • 3.1.2.2 Link databases & computing capacities
  • 3.1.2.9 Granularity through smart systems & edge compute
  • 3.1.2.11 Models to increase data granularity
  • 3.1.2.12 Solutions for private/public interests
  • 3.1.2.13 Procedures to aggregate sensitive data

3.1.4 Applications of AI techniques

  • 3.1.4.1 Identify key reference/training data sets
  • 3.1.4.2 Capitalize historical satellite data
  • 3.1.4.3 Privacy law solutions for satellite imagery
  • 3.1.4.4 AI handling heterogeneous & fuzzy information
Themes
Data infrastructure & governancePlant protection & pest/disease
Partners (2)
  • University of MalagaP1
    Coordinator

    SpainUniversity

  • Regional Ministry of Agriculture. AndalusiaP2

    SpainOther

#OrganisationCountryType
P1University of MalagaCoordinator

Introduction: PlagueAlarm provides a plant disease monitoring service for farmers and agriculture organisations (cooperatives, farm associations, and administrations) at the European level. In a first version, it focuses on Spilocaea oleaginosa (commonly called olive Repilo). PlagueAlarm is an extension of a Use Case developed within the Andalusian Technological Demonstrator for the Agri- Food sector, generated for the project “Towards the creation of the Federated Data Space of the Andalusian Agri-Food Sector (EDAAn)” funded by the Spanish RIA National call (PITEDS24-2024 SEDIA). The main objective of PlagueAlarm is to offer the Data Space as a Service (DSaaS) to the Agriculture of Data partnership, thus enabling an ecosystem to support the engagement of other use cases and scaling-up it to serve the sector in the Europe-wide context. Methodologies: The service integrates data from EUMETSAT, spectral indices derived from Sentinel- 2 satellite imagery and plague records from users into an AI model and extrapolated to the rest of the territory by kriging. The interaction with the user is done via a user-interface that displays a map to locate the user’s field plot and the results are displayed in maps. In addition, a mobile terminal APP will be adapted to capture geolocalized points of pest incidence, by farmers and/or field technicians in the cooperatives, both to feed the models and to validate them. Output: Digital risk maps of incidence and severity of Spilocaea oleaginosa (olive leaf scab) can be generated, from which alerts of high severity risk zones will be issued, obtaining time series of leaf scab evolution, and even obtaining predictive models from these data, considering specific olive tree varieties by area of predominance and incidence registered by official institutions. Impact: Farmers and organizations in the agri-food sector can now anticipate and issue alerts about potential crop disease risks. This will enable them to take measures to manage risks and apply treatments in affected areas. Disease control has a direct impact on plant health and production, both now and in the future. What I can offer: A data-driven disease and pest monitoring service that can be used under real- world conditions across Europe. This service can also be adapted for other pests affecting different crops within the framework of the AgData partnership. What I need: A community of stakeholders in the Agri-food sector register and join the PlagueAlarm application at the European level, making use of the resources. It is expected that new users will be able to test PlagueAlarm with new scenarios in different countries across different crops, such as almonds, fruit trees, pistachios, and other woody crops.

Source: Annual AgData Use Case Summit 2026 booklet.

Test and experimental facilities (TEF research) data
Models & Macro data
Statistics Registers data
Public administration data
  • 3.1.4.5 Data governance for farming data ownership
  • 3.1.4.6 Digital twins of farms & environments
  • 3.1.4.7 Strengthen AI uptake; trust in AI
  • Spain
    University
    P2Regional Ministry of Agriculture. AndalusiaSpainOther