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Global demand for food is anticipated to increase by 60% by 2050. Today, a great percentage of the world’s population is fed by small-scale farmers, primarily from developing countries, using traditional methods and rudimentary farming practices.
Global demand for food is anticipated to increase by 60% by 2050. Today, a great percentage of the world’s population is fed by small-scale farmers, primarily from developing countries, using traditional methods and rudimentary farming practices. The complex value chain and the lack of resources and connectivity add to the agricultural inefficiency, so, there is a strong need for a wider package of yield optimizing and risk decreasing services for these small-scale farmers. Project FARM, created at Capgemini’s Applied Innovation Exchange (AIE) Collaboration Zone (CoZone) in the Netherlands, aims to address these issues.
The Project FARM platform uses Artificial Intelligence to determine farming patterns through big data, generating insights from the data to make recommendations. It uses Machine Learning to make the platform applicable at scale by connecting it with cell phones. This solution has been built in collaboration with Agrics, a social enterprise operating in East Africa, which provides local farmers with agricultural products and services on credit.
The data and analysis of Project FARM are shown on a dashboard that provides useful insights. For example, farmers can access tailor-made advice to optimize crop production. Patterns from the available data provide Agrics with information that can help to steer commercial decision-making and provide insights into potential business risks. Information can also be provided to partners in the value chain, mainly providers of inputs (such as seeds and fertilizers) as well as producers and buyers, thereby eliminating inefficiencies.
Project FARM collects data from various public and private sources, sets it up in a cloud environment for hosting, and runs analytical models in the same cloud. Agrics offers data about crops grown, potential and realized yield, field perimeters, credit, and repayments. This information is combined with data from the Copernicus satellite. To facilitate the satellite data, project FARM is connected to project Sobloo, a Copernicus Data and Information Access Service (DIAS).
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