My research interests span both artificial intelligence and intelligent systems with a focus on computer vision and machine learning. My research concerns the design and implementation of self-aware and self-adaptive systems, which manage competing goals in terms of high-performance and low-power models for computer vision applications. I have worked on numerous challenging projects, such as developing novel data structuring and searching methods for efficiently and reliably searching a large number of digital items, developing an autonomous and power efficient system for use in unmanned aerial vehicles, and novel concept and solution that enables the analysis and visualisation of integrated spatial and temporal information. Currently, I am working on the world’s first fully-automated field phenotyping platform at Rothamsted Research. I work with a multidisciplinary team to design and develop novel applications and holistic models; as well as interpreting customised high-throughput heterogeneous data, including 3D Lidar, visible, thermal and hyperspectral imaging. The aim is to improve phenotyping capabilities for crop improvement through quantifying traits associated with yield and yield components.