Data remains fragmented as long as trees, harvest and storage are not connected
In the fruit supply chain, a great deal is already being measured, from flowering and growth in the orchard to quality in cold storage. With new sensors and technologies, it is now possible to collect data from each individual fruit tree on growth, flowering, and yield. This creates opportunities to enable data to flow between the different stakeholders in the chain, allowing it to generate real added value. The Orchard to Plate project demonstrates, through its first results in 2025, what is already possible and what is still needed to make this workable in practice.
From tree data to sorting only works if you can keep tracking your harvest
At two practical test locations, one in apples and one in pears, data was collected per tree on blossom, vigor, fruit set, and yield. This data is already valuable for growers to optimize cultivation. Its real value emerges when, during harvest, you can record which bin the fruit is placed in and where that bin is stored in cold storage. If this information is also available during sorting, it becomes possible to trace the origin of quality and plan more precisely with buyers. In that way, data shifts from simple recording to something you can actively base decisions on.
Broad knowledge question
When does data sharing actually lead to better yield and quality
The central question is how data can create more value across the entire fruit value chain, in terms of efficiency, sustainability and product quality. This only works if data can be shared between stakeholders and if it is clear who records which information and who acts on it. As long as data remains fragmented across separate systems, it is difficult to justify investments and to plan collectively. That is why this project focuses on showing when it works in practice and what is required to make it happen.
Approach
Demonstrating what works while exposing where it breaks down
The project runs from 2025 through 2027 and begins with testing and demonstrations at two practical locations. It examines what data-driven cultivation delivers in terms of efficiency, sustainability, and product quality. At the same time, data flows within the supply chain have been mapped to provide a clear overview of where information is collected and where it can create added value when shared.
Goal
Proving that the same harvest can be tracked from tree to sorting
The objective is to test and demonstrate that data from different systems can be used together. In practical terms, this means using consistent codes and naming so that a harvest batch can be tracked from tree to storage bin, from storage bin to cold storage and then through to sorting. This makes it clear what benefits this can deliver for growers, processors and retailers, and where the economic value is created. In doing so, the project helps move data use from optional to actionable, supporting better agreements and investment decisions.
Results and reflection
The first step towards a data-driven chain has been taken, but integration determines the real impact
In 2025, data was collected in the orchard, including blossom, vigour, fruit set and yield maps. Precision thinning had no effect this year because thinning agents did not perform as expected. However, the dataset did provide valuable insights into optimal fruit set and quality per tree in relation to blossom and growth. In addition, data flows across the fruit chain have been mapped, applications have been developed to exchange data between systems, and an initial dashboard has been created.
Achievements:
Data flows from orchard to cold storage have been mapped and shared.
Initial applications have been developed to exchange data between systems.
A first dashboard has been created and discussed in workshops.
Ideas for business cases have been gathered from growers and chain partners.
Key learnings:
Field validation remains essential, growing conditions determine whether a measure is effective.
Moving from measurement to use also requires clear agreements on data ownership, decision-making and value distribution.