DAS-CROPPER: How AIP Contributes to Future-Proof AI for the Agri Sector

The world of plant breeding is evolving rapidly. Sensors, drones, image recognition, genomic data, and high-throughput phenotyping are generating enormous amounts of data. But how do you turn that data into real value? And how do you ensure that AI not only delivers smart predictions, but that those outcomes are actually usable in day-to-day breeding practice?

These are exactly the challenges that Agri Information Partners (AIP) works on every day.

Together with Wageningen University & Research and partners within the DAS-CROPPER research project, AIP is helping shape the next step in data-driven breeding: a future in which data, AI, and software work seamlessly together.

The challenge: data is everywhere, consistency is not

Modern breeding programs generate data from many different sources at the same time. Think of field trials, greenhouse experiments, laboratory data, and image analysis from phenotyping platforms. The amount of information is growing rapidly, but it is often scattered across separate systems, Excel files, and different teams.

And that is exactly where the challenge begins.

AI can only deliver valuable insights when data is well-structured, reliable, and accessible. After all, AI is only as smart as the data and context it receives.

Jan Willem,Commercial Manager at AIP:

“Data is widely available today. The real value lies in how you connect that data and turn it into actionable insights.”

E-Brida as the foundation for breeding data

To address this fragmentation, AIP has been developing software for years that brings breeding data together within one environment. The E-Brida platform forms the central foundation for recording, managing, and analyzing data from breeding programs.

E-Brida is not only used by commercial breeding companies. Research programs at Wageningen University & Research also work with the platform. This creates an interesting synergy: researchers gain access to stable, practical software, while AIP remains directly involved in the latest developments in data, AI, and analytics.

Why DAS-CROPPER matters

An important step in this development is the public-private research project DAS-CROPPER (Data, AI, Standards – driving Crop Performance Prediction).

Within this consortium, companies, knowledge institutes, and research organizations collaborate on ways to make data smarter, safer, and more interoperable. NPEC is also involved in the consortium.

Within DAS-CROPPER, Wageningen University & Research and NPEC collect large amounts of different types of data from real breeding programs. This includes breeding data, genomic data, climate data, and field data. Advanced digital phenotyping platforms and measurement systems are used to analyze plants accurately and at scale.

Based on these combined datasets, Wageningen University & Research develops and validates AI models that can help identify patterns faster and improve prediction of crosses (genomic prediction) within breeding and crop research.

For AIP, the focus within DAS-CROPPER is on understanding and developing the right data infrastructure to support AI development in breeding. By actively participating in the project, AIP gains insight into how different data sources, such as phenotyping data, genomic data, climate data, and field data, can be reliably combined and made available for training and developing new AI models.

The project focuses on:

data integration between different systems,
standards for data exchange,
secure data spaces,
the use of AI for predictions in breeding and cultivation.

Translating AI research into practical software

For AIP, the strength of projects like DAS-CROPPER lies primarily in the direct connection between research and practice.

The knowledge developed within the consortium around AI, data architectures, and standards helps AIP further prepare software such as E-Brida for future AI applications. This includes better structuring of data, support for new data sources, and making AI outcomes accessible within existing breeding processes. This enables AIP to stay prepared for future developments.

In the coming years, AI is expected to play an increasingly important role in breeding and crop research. It will likely become a practical tool for identifying patterns faster, improving predictions, and supporting breeders more effectively in their daily work.

But achieving this requires more than just strong algorithms. The quality of AI ultimately depends on the quality of the underlying data, the way systems work together, and how insights can be translated into practical use.

This is where Agri Information Partners makes an important contribution. By actively participating in projects such as DAS-CROPPER, AIP explores what a future-proof data infrastructure should look like in order to optimally support AI development. At the same time, AIP translates new insights around data, standards, and AI into practical applications within software such as E-Brida.

In this way, AIP helps build the digital foundation needed to make AI truly valuable and applicable for the sector.