Big data integration
Description of the innovative solution
The integration of big data (for example, genetic and environmental/climatic data) can optimise agriculture. Big data entails the collection, storage and processing of large amounts of data, often in real time. Big data can be multi-source (e.g. images, videos, remote sensing data), multi-temporal (e.g. collected at different points in time), and multi-resolution (e.g. have variable spatial resolution). Big data is most useful when it is high quality, which means it is relevant, accurate and reliable, produced in a timely fashion, coherent, and accessible. Big data can be used to provide...
The integration of big data (for example, genetic and environmental/climatic data) can optimise agriculture. Big data entails the collection, storage and processing of large amounts of data, often in real time. Big data can be multi-source (e.g. images, videos, remote sensing data), multi-temporal (e.g. collected at different points in time), and multi-resolution (e.g. have variable spatial resolution). Big data is most useful when it is high quality, which means it is relevant, accurate and reliable, produced in a timely fashion, coherent, and accessible. Big data can be used to provide predictive insights in farming operations, improve decision making and farm extension services, drive real-time operational decisions, and redesign business processes for game-changing business models.
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John Deere ExactApply™
Lantek S.A.
Lantek S.A.
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Implementing Big Data in Agriculture
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