From Data to Trusted Data

Modern agriculture and food supply chains generate vast amounts of digital information. Sensors, farm management systems, laboratory analyses, logistics platforms and Artificial Intelligence models continuously produce great quantities of data that can improve productivity, sustainability and decision-making. One important aspect that is also a critical challenge in data governance can be summarized in one question: how can stakeholders trust that this information is authentic, has not been altered, and can be shared reliably across organizations and platforms? The AgriDataValue project (ADV) addresses this challenge by developing trusted and interoperable agricultural data ecosystems. Within this framework, the CHAINTRACK component leverages Blockchain technologies to support data certification, supply-chain traceability and interoperability across distributed systems. In this sense CHAINTRACK provides a technological foundation for data certification, supply-chain traceability and interoperability across distributed systems.

Building a Trusted Blockchain Infrastructure

The cornerstone of this trust framework is the implementation of a dedicated private Blockchain environment. It is based on an Ethereum-compatible architecture using a Proof-of-Authority (PoA) network with validator nodes and RPC nodes deployed through Kubernetes and Helm technologies. The architecture also supports several Blockchain platforms technologies, such as Ethereum, Polygon, Quorum and Avalanche.

Enhancing Supply-Chain Traceability

One of the most tangible applications of the ADV distributed ledger technology is supply-chain tracking. CHAINTRACK has implemented dedicated smart contracts and backend services designed to support traceability processes across agriculture chains. This allows creation of dedicated supply chains for specific user processes in relevant domains of activities. As for today, two of them have been produced, for meat and olive oil processes tracking. Through DLT-based recording mechanisms, stakeholders can create verifiable evidence that specific information existed at a given point in time. This approach can strengthen confidence in provenance data, processing records, quality certifications and other critical supply-chain information. Importantly, the system does not simply store business information. It combines business processes, databases and Blockchain certification procedures, creating an auditable trail that can be independently verified.

Supporting Trustworthy Artificial Intelligence

Another noteworthy innovation is the realization of an AI Model notarization capability. CHAINTRACK provides dedicated functionality for certifying trained AI models on Blockchain infrastructures.  As Artificial Intelligence becomes increasingly important in agriculture, for example in predictive analytics, crop monitoring, decision support and sustainability assessments, the governance of AI models is emerging as a critical topic. Stakeholders require mechanisms to verify that deployed AI models are authentic, untampered and correspond to approved versions. Blockchain-based notarization can contribute to these objectives by creating transparent records of model versions, digital fingerprints and related metadata. While distributed ledger technology is not a replacement for AI governance frameworks, it can serve as a complementary technology supporting transparency, integrity verification and reproducibility.

Generalized Data Notarization

CHAINTRACK AI notarization capability has been extended to support notarization of any digital asset (such as datasets, digital documents, contracts and others) that has been written in the ADV secure storage layer. This capability is highly relevant for data-driven agriculture, where trust in datasets is becoming increasingly important. Research organizations, technology providers, cooperatives and public bodies frequently exchange data whose provenance and integrity must be guaranteed. Blockchain-based notarization can provide an additional layer of confidence and security.

Inter-DLT Interoperability

One of the most forward-looking aspects added in CHAINTRACK is the strong focus on interoperability, declined in the domain of Blockchain services. Interoperability is often cited as one of the most important challenges for digital transformation in agriculture. Farmers, industry stakeholders, technology providers and public authorities typically operate independent systems that must exchange information securely and efficiently. CHAINTRACK tackles this challenge by exploring mechanisms capable of operating across multiple Blockchain environments. The available Inter-DLT functionalities enable cooperation with both external DLT ecosystems as well as with other instances of the ADV platform.

Looking Ahead

The available ADV Blockchain-based functionalities demonstrate how distributed ledger technologies can support trusted digital agriculture by providing a practical framework for enhancing trust in agriculture data ecosystems. As Europe continues to promote data-driven innovation, sustainability and digital sovereignty in agriculture, solutions that guarantee transparency, integrity and interoperability will become increasingly valuable. The ADV DLT infrastructure represents an important contribution to this vision, showing how trusted digital infrastructures can help transform raw data into reliable assets that support collaboration, innovation and value creation across the entire agricultural sector.

We are thrilled to share a new video presenting AgriDataValue Pilot #14. The pilot takes place in the Saint-Émilion region of southwestern France—a world-renowned, UNESCO World Heritage wine landscape where local wine growers are deeply committed to sustainable viticulture.

The Challenge: Spring frosts and climate volatility pose a severe threat to vine development, capable of destroying an entire harvest in a single event. While modern frost protection methods exist, they consume massive amounts of energy and water, making optimized resource use critical.

The Solution: Harnessing the power of data to master frost dynamics and prediction. By deploying weather stations and advanced soil/air sensors in frost-sensitive zones, the pilot monitors real-time temperature and humidity during critical stages like budburst. Integrating this field data with extensive historical records, machine learning models, and aerial thermal mapping allows the pilot to build precise frost risk maps and early prediction tools. This empowers wine growers to activate and place their protection systems with pinpoint accuracy, saving vital resources while safeguarding their yields.

Watch the full video insight here: https://www.youtube.com/watch?v=wSxxxgBYlu8

The Third Liaison Webinar for Horizon Europe sister projects, organised by AgriDataValue and the National Paying Agency under the Ministry of Agriculture of Lithuania, brought together European initiatives working on digital agriculture, Earth Observation, decision support systems, data sharing, and sustainable food systems.

Held online on July 3, 2026, the event proved highly valuable for knowledge exchange, engagement, and discussion. The webinar focused on sustainability, climate, and biodiversity, as the participating projects addressed topics including climate neutrality, agroecology, sustainability transitions, environmental monitoring, and biodiversity conservation. The following sister projects participated in the event: AgriDataValue, Carbonica, FarmBioNet, RURACTIVE, GEORGIA, Eco-Ready, and Smart-Era.  

The webinar highlighted the strong commitment of Horizon Europe sister projects to collaborate, exchange knowledge, and learn from one another by sharing best practices and lessons learned. A key takeaway from the discussions was that high-quality, accessible, and well-structured data is essential for the successful development and deployment of digital solutions, including AI and machine learning tools.

Overall, the Third Liaison Webinar demonstrated the value of cross-project collaboration in addressing the challenges facing modern agriculture. By bringing together expertise in soil health, circular economy approaches, consumer engagement, and data governance, the participating initiatives reinforced the importance of a coordinated approach to innovation. Together, these projects are helping shape a smarter, more resilient, and climate-neutral future for European agriculture.

We are honored to announce that AgriDataValue was invited by the European Commission to participate and present at the high-level online expert workshop, “Fostering AI uptake and scaling trusted AI solutions in agriculture,” held on June 24, 2026.

This prestigious event was jointly organized by the European Commission’s Directorate-General for Agriculture and Rural Development (DG AGRI) and the Directorate-General for Communications Networks, Content and Technology (DG CNECT). It served as a vital platform for structured exchange on accelerating AI adoption in the agri-food sector while strengthening European competitiveness and technological sovereignty.

The strategic importance of the event was highlighted by the active participation of leadership from both directorates. The workshop opened with introductory remarks from Catherine Geslain-Lanéelle (Director for Strategy & Policy Analysis, DG AGRI) and Thibaut Kleiner (Director for Future Networks, DG CNECT).

The webinar attracted a substantial audience of more than 280 participants, including representatives from EU institutions, farmers’ organizations, agri-tech providers, researchers, and policymakers.

AgriDataValue took center stage during Session 3: AI solutions in agriculture: benefits, maturity and scalability. Project Coordinator Dr. Theodore Zahariadis delivered a comprehensive presentation on the project’s innovations, showcasing their practical value for farmers and their potential for large-scale adoption across European agriculture.

The workshop focused on three principal objectives:

• – Presenting the state of play of AI uptake in the agricultural sector, highlighting developed use cases and their concrete benefits.
• – Discussing scalability and identifying the remaining technical, operational, and regulatory barriers to wider market uptake.
• – Gathering strategic input to shape future policy actions, initiatives, and stakeholder frameworks supporting the digital and data-driven transition of rural areas.

Concluding the session, Zoe de Linde (Deputy Head of Unit, DG CNECT) and Pierluigi Londero (Head of Data Governance, DG AGRI) outlined the next steps for integrating these expert insights into future EU data and AI initiatives.

The insights and discussions from this workshop will feed directly into a European Commission stakeholder input note to define priority use cases and infrastructure needs under the Apply AI strategy. AgriDataValue’s inclusion in this expert forum underscores the project’s significant role in shaping the future of trusted, sustainable, and data-driven smart farming in Europe.

We are thrilled to share a new video presenting AgriDataValuePilot #3.

The pilot takes place at the Vilscini 1 farm in the Zemgale region of Latvia—one of the area’s largest and most modern grain farms, managing over 2,000 hectares.

The Challenge: Plant diseases are a massive threat to crop yield and quality, heavily driven by unpredictable weather conditions.

The Solution: Merging traditional farming wisdom with cutting-edge tech.
By combining physical field observations and laboratory analysis with real-time data from on-site meteorological stations and soil sensors, the pilot integrates this information into advanced predictive models. This allows farmers to assess disease risk early, optimize crop management, reduce losses, and drive forward sustainable agriculture.

Watch the full video insight here: https://www.youtube.com/watch?v=pz6J2kgg2Tc&t=2s

Climate change is increasing the frequency and complexity of weather-related risks across agricultural systems. Farmers are facing a growing combination of challenges, including droughts, heatwaves, frost events, and changing rainfall patterns. At the same time, these hazards are becoming more difficult to anticipate because they are influenced by multiple environmental factors that interact over time. Traditional risk assessments often rely on a limited number of indicators, such as air temperature or rainfall totals. While these metrics remain important, they do not always capture the full picture of how climate-related impacts develop in agricultural environments. As climate conditions become more variable, there is a growing need for approaches that can integrate different types of environmental information and transform them into actionable insights.

Within the AgriDataValue project, innovative data-driven methodologies are helping to address this challenge by combining sensor observations, climate records, and environmental indicators to improve the understanding and prediction of climate risks.

Looking Beyond Single Indicators

Agricultural systems are influenced by a wide range of environmental conditions that rarely operate in isolation. A damaging event is often the result of several factors acting together rather than a single threshold being exceeded. For example, frost damage is commonly associated with low temperatures. However, the severity of an event may also depend on atmospheric moisture, humidity levels, surface wetness, crop development stages, and local environmental conditions. Similarly, drought impacts are not determined solely by how much rain falls over the course of a year. Temperature increases, seasonal rainfall distribution, and soil moisture dynamics all play important roles in determining water availability for crops.

Recognizing these interactions is essential for improving risk assessment. By bringing together multiple sources of environmental information, it becomes possible to better understand the conditions that lead to crop stress and damage.

Combining Data to Better Understand Climate Risks

One of the key lessons emerging from recent analyses is that environmental data becomes significantly more valuable when different sources are considered together. Weather observations, sensor measurements, and historical climate records can each provide important insights on their own. However, when combined, they reveal relationships and patterns that would otherwise remain hidden. Accordingly, this integrated approach makes it possible to detect environmental conditions associated with increased climate risk, identify combinations of factors that contribute to crop vulnerability, understand how conditions evolve before a damaging event occurs, improve the accuracy of risk assessments and early warning systems and support more targeted and informed management decisions.

Rather than focusing on a single measurement, the methodology considers the broader environmental context in which agricultural production takes place.

Understanding Spatial Variability

Climate risks are rarely distributed evenly across agricultural landscapes.

Even within the same production area, environmental conditions can vary considerably due to differences in elevation, slope, exposure, soil characteristics, and local microclimates. As a result, some locations may consistently experience higher levels of risk than others.

Data analytics can help identify these patterns by analysing observations collected across multiple monitoring points. This makes it possible to distinguish areas that are repeatedly more vulnerable from those that are naturally more protected.

Such information is particularly valuable for farmers and land managers because it supports more precise interventions. Instead of applying the same measures everywhere, resources can be directed toward the locations where they are likely to have the greatest impact.

Learning from Historical Events

Historical climate records provide another important source of knowledge.

By analysing past events and comparing them with current environmental conditions, it becomes possible to identify recurring patterns and better understand how risks develop over time. This allows decision-support systems to recognise situations that resemble previous damaging events and provide earlier indications of potential problems.

The approach is particularly relevant in the context of climate adaptation. As more data becomes available, historical knowledge can be continuously enriched, enabling more robust risk assessments and improving the ability to anticipate future events.

Revealing Long-Term Climate Trends

In addition to supporting the detection of short-term hazards, integrated data analysis can also help identify long-term changes in climate behaviour.

Recent assessments have highlighted several important trends affecting agricultural systems:

• – Increasing average temperatures;
• – Greater variability in rainfall patterns;
• – More frequent periods of water stress;
• – Longer and less predictable drought seasons;
• – Greater occurrence of compound climate risks.

These findings demonstrate that climate vulnerability is influenced not only by individual extreme events but also by gradual changes that accumulate over time.

Understanding these trends is essential for designing effective adaptation strategies and ensuring the long-term resilience of agricultural production systems.

From Data Collection to Decision Support

The value of agricultural data lies not simply in collecting information but in transforming that information into knowledge that can support decision-making.

By integrating environmental observations, climate indicators, historical records, and analytical methods, it becomes possible to move from reactive responses toward more proactive risk management. Farmers, advisors, and agricultural organisations can gain a clearer understanding of emerging threats and take action before impacts become severe.

This transition from data collection to climate intelligence is a key objective of AgriDataValue. By unlocking the value of diverse data sources, the project contributes to the development of practical tools that support resilience, sustainability, and informed decision-making across the agricultural sector. The work carried out within AgriDataValue demonstrates that combining environmental data, climate information, and advanced analytics can provide a deeper understanding of agricultural vulnerability and support more effective adaptation strategies.

By turning data into actionable intelligence, these approaches help pave the way toward more resilient, sustainable, and climate-ready agriculture.

As part of the project’s research activities, and specifically within Use Case 3.1: Disease Forecasting and Detection, a field demonstration was conducted in Messinia, Greece, at the AgriDataValue pilot site.

The field demonstration, organized by the NILEAS Producers Group in a traditional olive grove, showcased how advanced digital technologies can enhance the early detection and management of olive anthracnose (Colletotrichum gloeosporioides). The event demonstrated the value of real-time monitoring and predictive analytics in providing growers with timely warnings, enabling them to optimize the timing of crop protection measures and improve disease management practices.

Use Case 3.1 leverages SynField smart agriculture systems to collect and analyze real-time data for monitoring disease epidemiology and supporting early disease detection. Through the AgriDataValue framework, predictive models are developed to forecast and identify diseases affecting fruit trees. By integrating IoT-based weather, soil, and leaf wetness sensors, the system continuously monitors key environmental parameters, including microclimatic conditions and canopy moisture, and generates automated disease risk indices to support farmers’ decision-making.

On Wednesday, 17 June, NILEAS organised a workshop on Use Case 3.4 “Pest control on olive fruit fly” at the Messinian Chamber of Commerce and Industry as part of the AgriDataValue project (Horizon Europe).

The workshop focused on the management of the olive fruit fly (Bactrocera oleae), one of the most destructive pests affecting olive production. In recent years, its control has become increasingly challenging due to the predominance of olive monoculture, the impacts of climate change, and the withdrawal of active substances under the European Green Deal. These challenges have resulted in significant economic losses for olive growers and a deterioration in olive oil quality, highlighting the urgent need to evaluate and modernise the Greek National Olive Fruit Fly Control Programme.

The event began with a presentation by NILEAS agronomist Nikos Petoumenos, who introduced the AgriDataValue project, in which NILEAS participates as the Greek pilot organisation for olive cultivation. He explained that the project, which entered its fourth year in February 2026, focuses on developing innovative digital tools for precision agriculture. He presented the three use cases implemented by NILEAS, covering the prediction, monitoring, and management of olive anthracnose (Colletotrichum gloeosporioides),frost, and the olive fruit fly (Bactrocera oleae). Emphasis was placed on Use Case 3.4, which aims to improve olive fruit fly monitoring and management by using a machine learning (ML) modelthat integrates data from IoT sensors, automated traps, and other digital technologies to support timely, informed pest management decisions.

Outcomes:

• The sustainability of olive cultivation depends on the collective adoption of modern cultivation practices, appropriate mechanisation, and digital technologies, helping to address labour shortages, improve management of steep and fragmented olive groves, and reduce production costs.
• Participants considered UC 3.4 highly relevant and recommended uploading field sampling results to the AgriDataValue platform to improve the reliability, accuracy, and long-term performance of the system.
• Integrating digital olive fruit fly traps with weather data enables predictive pest risk assessment rather than simple pest detection. This is particularly valuable in Greece, where olive fruit fly development is strongly influenced by weather conditions, supporting more effective and timelier pest management. Expected impacts:
• Improved pest forecasting through early detection of olive fruit fly outbreaks, better understanding of population dynamics, and identification of high-risk periods.
• More precise decision-making by enabling targeted interventions and optimising the timing of control measures.
• Reduced pesticide use through threshold-based treatments, minimising unnecessary applications and pesticide residues.
• Lower production costs by reducing labour, pesticide use, and the overall cost of pest management.
• Higher olive yield and quality through reduced fruit damage, improved olive oil quality, and fewer secondary fungal infections.
• Smart pest management by adapting control strategies to weather conditions and enhancing the resilience of traditional rainfed olive groves.
• Environmental benefits through optimised pesticide use, protection of biodiversity, and support for the implementation of IPM principles.
• Digital decision support with automated alerts, integration with Decision Support Systems, and real-time monitoring.
• Benefits for cooperatives and advisory services through area-wide monitoring, regional risk mapping, improved recommendations, and coordinated pest management.

Participants emphasised the importance of research projects that address real-world needs, ensure the effective dissemination of results, and provide a solid basis for anticipating future challenges and developing evidence-based solutions, as demonstrated by the AgriDataValue project.

The workshop proved highly productive, bringing together farmers, agronomists both private and public, and representatives of local authorities. Participants reached a common understanding of the main challenges facing olive cultivation and agreed that combining digital olive fruit fly traps with weather data transforms pest monitoring from a reactive to a proactive approach, enabling earlier, more targeted, and more sustainable interventions.

The AgriDataValue (ADV) project was presented at the 6ᵗʰ Congress of Geographical Information Systems and Spatial Analysis in Agriculture and Environment, held at the Agricultural University of Athens from 19–21 May 2026.

The congress brought together researchers, institutions, and technology providers working on Geographic Information Systems (GIS), spatial analysis, remote sensing, and precision agriculture applications. Within this context, AgriDataValue showcased its approach to integrating geospatial intelligence, IoT data, and advanced analytics into modern agricultural management.

The presentation highlighted how GIS technologies play a central role in the AgriDataValue ecosystem, supporting the collection, visualization, and analysis of agricultural and environmental data. Through the use of spatial data infrastructures, satellite imagery, sensor networks, and AI-driven analytics, the project aims to improve decision-making, resource efficiency, and environmental monitoring in agriculture.

In parallel, Synelixis sponsored the event and participated in the congress exhibition area with a dedicated stand, where AgriDataValue brochures, roll-up banners, and SynField devices utilized within the project were presented to participants and the broader audience of the congress. The stand offered visitors the opportunity to learn more about the practical implementation of smart farming technologies and their contribution to data-driven agricultural management.

More information about the congress is available at:
6ᵗʰ GIS Congress Official Website

AgriDataValue was recently featured at the regional event, “Presentations of Innovative and Research Capabilities in the Agri-Food Sector – Experiences from the Implementation of Research and Innovation Projects in the Region of Central Greece during the 2014–2020 Programming Period,” organized by the Region of Central Greece.

The event was held on Thursday the 14th of May 2026 and brought together key stakeholders to explore how research and innovation can drive sustainable development across the agri-food sector. Discussions centered on critical themes, including the circular bioeconomy, food authenticity and traceability, sustainable food systems, and the digital transformation of agriculture.

Within this framework, AgriDataValue was presented, by NKUA, as a flagship example of ongoing European research and innovation. The presentation highlighted the project’s deployment of cutting-edge technologies—such as Artificial Intelligence (AI), IoT sensors, satellite data, and smart data-driven tools—to advance precision agriculture, sustainable resource management, and intelligent decision support systems.

Furthermore, the session illustrated how these advancements directly contribute to boosting productivity, enhancing food quality, and strengthening the overall competitiveness and sustainability of the agri-food sector throughout Central Greece.