PhD Studentship: From Pixels to Phenotypes: Using AI and Large Language Models for Precision Plant Specimen Image Analysis

There are hundreds of millions of specimens housed in libraries (herbaria) worldwide. Many are digitized and is easily accessible online, but the information contained within these images remains less accessible, as the analysis of specimens typically requires physical measurements, interpretation of in-hand or digitized specimens, or invasive methods.

This project will explore standard protocols for digitising analytical imaging of specimens, focussing on carnivorous plants. It will develop artificial intelligence and statistical analysis methods to automatically measure components of plant phenotype (e.g., morphology, flowering). Considering that the performance of existing deep learning based object detection models fails to generalise well to new data, this project will further investigate the possibility of using pre-trained LLMs (such as BERT (Jia et al. 2022) and Alpaca-7B) as prior knowledge to improve the detection performance. The effectiveness of AI models will be compared with manual evaluations conducted on optical images and destructive methods.

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