Sustainable Agriculture with Autonomous System Technologies
Find out about our research on agriculture's role in addressing global food security and sustainability challenges through the integration of artificial intelligence.
Precision agriculture aims to produce more food but with reduced usage of natural resources so tackles the global challenges in food security and sustainability and helps to address the climate change. Significant research profile has been built up in the Centre in applying autonomous system technologies into precision agriculture.
AI, automation, and other information technologies will potentially revolutionise our agriculture and food production systems. We are developing new remote sensing capability enabled by satellites, UAV and ground robots, and integrating them with other available data sources of weather, soil and crops to provide unprecedented multiscale, high temporal and spatial sensing and monitoring capability for agriculture and horticulture applications. Many real-world applications have been investigated, including detecting and mapping weeds, pests, diseases, drought, and crop growth. Unmanned aerial and ground vehicles and related technologies are also developed for chemical spraying and crop intervention.
So far, over 10 projects with a total budget of about £4M have been funded by STFC, BBSRC, and Innovate UK. These projects aim to reduce the use of resources (e.g. water, land) and the environment impact of using fertiliser, pesticides, and herbicides by providing real-time site-specific information for targeted variable rate of treatment and crop management. The technologies can find a wide range of applications beyond precision agriculture (e.g. wild animal monitoring, and environment protection). Furthermore, autonomous system technologies provide a promising solution to cope with social challenges faced in agriculture; for example, shortage of seasonal labours in developing countries like UK.
Research in this area is of a significant multidisciplinary nature and often requires a multi-institutional team. We work widely with organisation in the UK and internationally.
References
J Su, W Yi, C Liu, B Su, X XU, L Guo and W-H Chen (2021). Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring. IEEE Transactions on Industrial Informatics. Vol.17, Issue: 3. Page(s): 2242-2249. DOI: 10.1109/TII.2020.2979237.
J Su, M Coombes, C Liu, Y Zhu, X Song, S Fang, L Guo, and W-H Chen (2020). Machine Learning-Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery. Unmanned Systems. Vol.8, No.1, pp.71-83. Best Paper Award.