Empowering Agritech via Big Data Analytics on Supercomputers
Precision Agriculture and Precision Livestock Farming are assisting in the optimisation of agricultural and livestock production whilst minimising waste and operational costs. Data from a range of sensors in fields and crops can be mined to provide granular information on soil conditions. Drones can patrol fields and alert farmers to crop ripeness or potential growth problems and images from satellites can be processed using artificial intelligence to provide a holistic view of the crops at the individual field level. The CYBELE project is defining the High Performance Computing infrastructure to enable analysis and fusion of Big Data to revolutionise production practices in the agricultural sector.
The Department of Electronic and Electrical Engineering at the University of Strathclyde (Glasgow, Scotland, https://www.strath.ac.uk/engineering/electronicelectricalengineering/) is leading the CYBELE demonstrator on 'Autonomous Robotic Systems within Arable Frameworks', in collaboration with the BioSense Institute (Novi Sad, Serbia, https://biosens.rs/) and key partners in the UK Agri-Tech sector, including Soil Essentials (https://www.soilessentials.com/) and the Agri-EPI Centre (https://agri-epicentre.com/). The team is developing solutions based on Machine Learning that enable the deployment of autonomous vehicles that both monitor crop condition and forecast crop yield at early stages in the growth cycle as well as providing the control to execute on routine tasks such as the application of fertilisers.
The animation below provides a summary of the demonstrator: