Documenting plant diversity through taxonomic publications is crucial for conservation, without understanding which species are present, it is not possible to protect them or to evaluate their potential significance. In the worldwide network of over 3000 herbaria, millions of herbarium specimens are preserved records of plant diversity. However, the accurate identification of these specimens is a time-consuming process, due to the large volume of specimens, challenges in taxonomically difficult groups and a decreasing number of experts. Increasing the speed that taxonomic outputs are produced is key to acting against the threats to the world’s habitats. This project aims to accelerate taxonomic efforts in the genus Cyrtandra by developing image recognition methods to automatically identify specimens. An easy-to-use software system will be built to classify plant specimen room images into different tags with confidence values, thereby speeding up the discovery of new species.
Before starting my PhD, I worked at a university in China, primarily providing IT support and managing and maintaining systems. I earned my master’s degree in Artificial Intelligence at the University of Southampton.
After working, I discovered that I still had an interest in researching the applications of artificial intelligence, and I wanted to further my studies in this field. Therefore, I began looking for opportunities to continue research.
CENTA provides a wide range of doctoral training and offers interdisciplinary collaborative projects.
The training provided by CENTA and Loughborough University is incredibly beneficial for future career paths, whether one chooses industry or academia. This training, especially in enhancing personal abilities and networking skills, is invaluable. I aspire to pursue a career in the application of artificial intelligence in the future, not limiting myself to just industry or academia. More importantly, I hope that my work can contribute and serve as a reference for numerous research endeavours.
