Agrimetrics Data Marketplace is a tool that focuses on supply levels in the food system. It was developed in 2019 in response to realising the value trapped in the data of agri-food organisations (“About Us | Agrimetrics” 2023). It helps to address some of the core challenges in the agri-food sector with AI at the supply stage (including data access and data sharing) by functioning as a collection of linked agri-food data that can be easily shared, accessed, and monetised (Barclays 2020, 32). Agrimetrics is used in multiple ways, and is particularly interesting as a use case as it helps to address some of the issues with data sharing and accessing during the supply stage by deepening integration of data into agrifood processes. Agrimetrics Data Marketplace is predicated on an open architecture to allow users to access a breadth and depth of data in one place, in one interface that is easily searchable. Moreover, the use of AI to collate and harmonise data from multiple sources saves extensive time in data transformation. Data exchange privacy is also able to be controlled by the user whilst help is given to manage the integrity and security of data and data governance systems (Barclays 2020, 32). An example of a use case of Agrimetrics is the predictive service of CropLens AI (“AI Identifies Crops from Space with 90% Accuracy” 2022). This is a proprietary algorithm that identifies crop types from space using Machine Learning algorithms. It offers predictions of the types of crops that are growing in the UK, based on a model developed by Agrimetrics and observations of field boundaries from space from Sentinel-1 Synthetic-Aperture Radar (SAR). It then assigns the crops into 5 categories of Oilseed Rape, Winter Wheat, Winter Barley, Grass and “other” to ascertain supply levels (“CropLens AI” 2024). CropLens AI is allowing easier, faster and more affordable insights to be generated much earlier in the season: it offers near real time predictions of the types of crops growing in the UK since predictions are updated every 6 days, in accordance with attributing the latest SAR acquisitions to UK fields. And the overall model precision of the algorithms used in CropLens AI is 67% in October (beginning of the growing season), but increases to 90% in July, August and September (end of the growing season). Part of this high precision is due to the algorithms being trained and tested against real world observations, made possible by the wide spread of data captured by Agrimetrics. Usage of the tool is also increasing amongst retailers. For instance, Airbus now use Agrimetrics as an exclusive reseller of their whole earth observation portfolio (this includes their Airbus Crop Analytics, an advanced set of crop/field analytics that includes leaf area index, leaf water content, and soil water saturation) for the UK agricultural market. Moreover, Agrimetrics does not only help with yield prediction, but hosts multiple different AI tools and datasets across its platform, including solutions to disease and pest prediction, optimising nitrogen applications, and irrigation (Morrison 2020). ClearSky, a University of Hertfordshire founded cutting-edge AI outfit, also uses the Data Marketplace to market uninterrupted optical data to calculate measures such as Normalized Difference Vegetation Index. Moreover, Agrimetrics provides tools and support for code developers who join the marketplace, allowing them access to documentation and sample data, which allows for high levels of engagement with the tool with minimal buy in needed upfront. This suggests that increased used of the platform could be very viable. Agrimetrics has been appointed the Data Innovation Partner to the Agriculture and Horticulture Development Board (“AHDB Appoint Agrimetrics as Data Innovation Partner” 2022), and received a substantial initial seed fund investment of £90 million from the UK’s strategic innovation agency, Innovate UK (Barclays 2020, 32). This has made Agrimetrics one of four centres for agricultural innovation, and helped enable Centres to build new infrastructures and innovation and capitalise on leading UK research and expertise (e.g. Crop Health and Protection, Centre for Innovation Excellence in Livestock, Engineering and Precision Technologies, and Agrimetrics) (“Agri-Tech Centres” 2021). These centres are intended to allow industry to cooperate with and access new research, and may provide a useful model to how, outside of academic publishing, innovation in AI in food is being implemented at scale.