Lead DIGIT Lab Academics: Professor Gerard Parr, Dr Hane Aung, Dr Ben Milner, Dr Edwin Ren, Dr Yusuf Tukur
We are focused on how to initiate environmental settings to accelerate plant growth, and derive optimal environment settings for plant growth, by learning from the previously collected data. We aim to determine which methods are best for making informed decisions, and justifying the potential benefits of digital transformation to agribusinesses (efficiency and timesaving). We deployed IoT sensors in farm fields collecting data about plants and the environment, and applied AI to analyze the data (e.g. plant health, nutrient/moisture requirements, current environment state) to help farmers increase efficiency and optimal productivity.
UEA worked with Agri-TechE delivering a workshop to understand the current digital landscape within agri-tech business and the expectations of businesses embarking on a digital journey and how digital technology adoption impacts their efficiency and productivity. A solution is being developed at the UEA (smart seed testbed), to address key challenges identified from the workshop. A full report was produced and research for publication is ongoing. Read the full report
Selected Publications
- Ghamdi M., Parr G., Wang W. (2024). Heterogeneous Machine Learning Ensembles for Predicting Train Delays. IEEE Transactions on Intelligent Transportation Systems, DOI.
- Hsieh C., Ren Y., Chen J. (2023). Edge-Cloud Offloading: Knapsack Potential Game in 5G Multi-Access Edge Computing. IEEE Transactions on Wireless Communications, DOI.
- Brewer, S., Manning, L., Bidaut, L., Onoufriou, G.Durrant, A., Leontidis, G., Jabbour, C., Zisman, A., Parr, G., Frey, J., Maull, R. (2023). Decarbonising our food systems: contextualising digitalisation for net zero. Frontiers in Sustainable Food Systems. DOI.
- Ren Y., Phung-Duc T., Chen JC, Li FY. (2023). Enabling Dynamic Autoscaling for NFV in a Non-Standalone Virtual EPC: Design and Analysis. IEEE Transactions on Vehicular Technology, 1–14. DOI.