Digital labeling and data management for agriculture
EU regulations are mandating machine-readable digital labels for plant protection products. Static PDFs no longer comply. Data Graphs is the chosen technology for CropLife Europe's AgriGuide initiative, delivering compliant, GS1-compatible, machine-readable labels at scale across 27 EU member states and 25 languages.
The missing layer in your agricultural product data ecosystem
Crop protection manufacturers operate in one of the most complex regulatory environments in the world. A single plant protection product may have different approved uses in different EU member states, different application rates for different crops, different safety requirements for different operator categories, and different label formats mandated by different national authorities. This data lives across registration databases, R&D systems, regulatory submissions, and legacy PDF documents, in some cases spanning decades of accumulated product history. Managing it manually is the status quo. It is also why digital label accuracy problems persist, why label updates take weeks rather than hours, and why compliance teams spend disproportionate time on data reconciliation.
Data Graphs provides the reference data backbone that connects all of this. Formulation data from R&D, registered uses from national approval databases, Good Agricultural Practice tables, operator safety requirements, and label content are all connected in a single governed knowledge graph. When a product's approval changes in one market, the label content updates automatically. When a new crop registration is added, it propagates through the label without manual re-entry. AI-assisted workflows help teams extract structured data from legacy PDFs, turning decades of unstructured documentation into a machine-readable, queryable product knowledge base. The result is machine-readable, regulator-compliant label output at a scale and speed that no manual process can match.
Publish a compliant digital label in hours, not weeks
Under the current process at most crop protection manufacturers, updating a label (adding a new crop registration, reflecting a changed safety requirement, or incorporating a new formulation) involves retrieving data from multiple systems, editing a document, reviewing through multiple regulatory and commercial stakeholders, and re-publishing in each affected market. For a portfolio with hundreds of products across 27 EU member states, this process runs continuously and consumes significant resources. With Data Graphs, the label is not a document. It is a set of data relationships in the graph. When a registration changes, the graph updates. The label output, machine-readable, GS1-compliant, formatted for the AgriGuide platform, regenerates automatically. What was a multi-week editorial and validation process becomes a governed, auditable data update.
Give your team an AI assistant that knows the entire AgriGuide data model
The AgriGuide data model is extensive and complex: registered formulations, Good Agricultural Practice tables, crop-specific application rates, operator categories, buffer zones, and environmental risk assessments all interact in ways that are difficult for a human expert to hold in mind. Data Graphs includes a built-in AI assistant that is an expert on the AgriGuide schema and the product data loaded into your platform instance. Regulatory affairs teams can ask questions in natural language, such as "Which of our products need a label update in France following the recent approval change?" or "What are the current GAP requirements for this active substance on winter wheat in Germany?", and receive accurate, sourced answers drawn from the governed product knowledge graph, without needing to navigate multiple databases manually.
Connect upstream product data to downstream label output, once
The data supply chain for a digital label begins in R&D, passes through regulatory affairs, is enriched by commercial and marketing teams, and culminates in publication on the AgriGuide platform. At most manufacturers, this chain is managed through a series of handoffs between people, systems, and file formats. Data Graphs replaces that chain with a connected graph: R&D formulation data, regulatory approval records, GAP tables, and label content are all connected as nodes and relationships in a single knowledge model. Each team works in its domain; the connections between domains are maintained automatically by the graph. Downstream label output is always derived from the current state of the upstream data. There is no manual reconciliation step, no risk of a label reflecting an outdated approval status, and no dependency on a single expert who knows where all the data lives.
“Data Graphs has been instrumental in making AgriGuide a success. What are today flat electronic documents such as PDFs are now converted to structured data products that can help power a safer, greener, and more resilient food system across the EU.”
See how Data Graphs powers digital labeling for European agriculture.
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