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    Tuberscan: Using deep learning and data science to improve agronomy decisions in the potato industry


    The aim of this project is to increase efficiency and productivity in potato production, while minimising environmental impacts, by creating a farming system based on data integration, artificial intelligence and enginnering technology.


    Determining the correct date on which to harvest potatoes is one of the most critical decisions potato growers must make. If they lift their potatoes too early, they may be below the optimum size resulting in less than the maximum potential output being produced, if they lift them too late, they may be too large to meet buyer specifications making them unsaleable. Either way the grower loses potential income. A recent proof of concept Innovate UK project (TUBERSCAN) has shown that it is possible to use new technologies to non-invasively measure the total biomass of potato tubers in the soil. Combining this with above ground data of potato plants, number of tubers per potato plant can be accurately determined. In addition, research has been conducted to create a cost-effective technology solution that supports the above on a commercial platform.

    The aim of this project is to build on the findings from the TUBERSCAN pilot project to develop and test an innovative demonstrator system to measure and map average tuber sizes and yield throughout potato fields. This data will provide insights to will drive early interventions and/or selective harvesting to take place, thereby optimising crop yield and resource use. It is anticipated that this technology could generate an estimated 5 - 10% increase in UK marketable potato production, while assisting with reducing waste throughout the supply chain, working towards net zero emissions in the potato industry.

    Funding Body

    Innovate UK

    Lead Organisation

    Harper Adams University


    B-Hive, Branston, University of Manchester

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