Big Data refers to the generation of very large diverse data sets obtained from digital measurement, collection and storage technologies. Examples in agritech are datasets from remote and in-field sensors, devices, satellite & GPS networks, log files, transcriptional applications, image and audio files, web, and social media – much of it generated in real time. These datasets are so expansive, they cannot be quickly assessed by conventional analysis techniques. Advanced analytics techniques are required to process agritech big data to make it accessible, understood and actionable for growers.
Big Data analytics integrates data from diverse sources and examines it to uncover information such as hidden patterns and unknown correlations, to generate novel insights enabling informed decision making.
How it works
Advanced algorithms for machine learning, predictive analytics, data & text mining are needed to unlock valuable insights from big data. Data sets such as text, image and audio undergo classification, regression and similarity matching processes, and output is compared to the definition of what is desired so that the likelihood of future outcomes can be predicted.
Farmer / Agronomist benefits
Being evidence based and founded on significant datasets, output from big data analytics is powerful and can give significant competitive advantage to UK growers in the marketplace.
As systems become more scalable and affordable it will change the way farms are operated and managed. Predictive insights into farming operations will be developed, driving real-time operational decisions to enable smarter management of key resources including seed, fertiliser, and pesticides while increasing productivity. Cost savings and better supply chain management will also be facilitated.
Key Researchers and Stakeholders