Plant variety registration in the EU is governed by a framework built around field trials. Distinctness, Uniformity, and Stability — DUS testing — requires growing candidates under observation across multiple seasons and sites. Value for Cultivation and Use testing adds performance evidence. None of this was designed with AI-generated predictions in mind, and as sequence-based characterisation tools become more capable, the question of where they are — and are not — accepted as regulatory evidence is increasingly relevant for breeders who use our platform.
Where the Framework Currently Stands
The honest starting point is that AI-generated trait predictions currently have no formal evidentiary status in EU variety registration procedures. The Community Plant Variety Office framework and the national registration systems that feed into it require phenotypic observation data from standardised trial conditions. A prediction score from a genomic model, however accurate, is not a substitutable form of that evidence under current regulations.
This does not mean the technology is irrelevant to the registration pipeline. What it means is that the role of AI prediction is, right now, upstream of the regulatory process — in candidate selection, in prioritisation for trial slots, and in pre-screening that reduces the number of lines that enter the formal testing cycle. These are valuable roles, and breeders who use genomic prediction effectively can enter more competitive candidates into DUS and VCU testing. But they cannot bypass or abbreviate those trials based on prediction evidence alone.
Molecular Markers: The Precedent That Matters
The question of whether genomic data can play a formal role in variety characterisation is not entirely without precedent. The CPVO and UPOV have been developing guidance on the use of molecular markers in DUS testing for several years. The current position — and this is actively evolving — is that molecular marker data can be used to support distinctness determination in a supplementary role, particularly for verifying variety identity and detecting near-identical candidates. Markers are not accepted as a replacement for morphological observation, but they are accepted as corroborating evidence.
AI-based trait predictions are a further step removed from direct genetic characterisation than marker panels are. A marker is a direct measurement of a specific locus. A model prediction is an inference drawn from a complex function applied to sequence embeddings. Regulators who are cautious about molecular markers are, reasonably, more cautious about model outputs. But the marker precedent matters because it establishes that genomic data — in some form, under some conditions — can contribute to the regulatory record. That door is open, even if the specific room for AI predictions has not yet been furnished.
The EU AI Act and Agri-Food Implications
The EU AI Act, which came into full effect in 2025, introduced a risk-classification framework for AI systems. Systems used in "safety-critical" applications in sectors including food and agriculture are subject to higher conformity obligations. The key question for genomic prediction platforms is whether a prediction used to inform a variety registration decision would be classified as high-risk under this framework.
Our current reading — and we have taken legal advice on this, though we are not publishing that advice here — is that predictions used to prioritise internal breeding programme decisions (which lines to advance, which to drop) are unlikely to be classified as high-risk. Predictions that directly contribute to a regulatory submission, by contrast, are closer to the boundary and may attract conformity obligations including documentation of training data, validation methodology, and performance characteristics.
We have begun building the documentation infrastructure for this use case, not because it is a current requirement for our customers but because we expect it to become one as AI-assisted characterisation becomes more common. The kind of documentation the AI Act would require — model card, training data provenance, validation dataset description, known failure modes — is also just good practice for a scientific tool, so there is no tension between compliance preparation and scientific rigour.
National Variation: What Changes Between Member States
EU variety registration is not fully harmonised. While the CPVO handles Community Plant Variety Rights for species listed under its jurisdiction, many breeders seeking national list registrations for specific member states deal with national competent authorities that have latitude in how they implement DUS and VCU testing. This creates variation in openness to novel evidence types.
In conversations with breeders and registration consultants across several member states, we have seen a spectrum. Some national authorities are actively exploring how molecular and computational evidence might streamline certain elements of DUS testing — particularly for uniform varieties where genetic identity verification could reduce the years of field observation required. Others maintain that morphological observation is the only admissible evidence and are not currently considering extensions.
For breeders, this means the regulatory utility of AI prediction data depends partly on which national system they are registering with. We advise customers not to assume that what is possible in one member state is possible in another, and to engage with their national authority early in the process before designing their data collection strategy around any particular evidence model.
The Timeline Question
The DUS testing cycle — typically two to three years of field observation — represents a significant commercial delay for breeders. There is genuine industry interest in whether AI-based characterisation could compress this timeline, either by providing early evidence that a candidate is distinct from registered varieties, or by accelerating the stability determination through sequence-based uniformity analysis. The regulatory institutions are aware of this interest.
UPOV has working groups exploring the role of molecular and AI-based tools in DUS testing methodology. The CPVO has published consultation documents on the topic. The pace of formal adoption of new evidence types in variety registration has historically been slow — measured in decades rather than years — but the intensity of attention to this question has increased noticeably in the last eighteen months. We are participating in one of these consultation processes and hope to have more concrete information about the direction of travel later this year.
Our Position
We are a prediction platform, not a regulatory consultancy, and we want to be clear about the limits of our expertise in this area. What we can say is that we are building our platform documentation and validation infrastructure in anticipation of a regulatory environment where AI-generated evidence will need to meet formal quality standards. We believe that environment is coming — the question is the timeline, not the direction.
In the meantime, the value our platform delivers is in the internal breeding process, before registration. Better candidate selection means more competitive entries into registration trials, and higher success rates in those trials. That is a real and measurable benefit within the current regulatory framework, without requiring any change to the rules. We do not need AI predictions to be accepted by regulators for our customers to benefit from them.
