AI-Assisted Quality Checks for Herbarium Sheet Digitisation

It is estimated that there are nearly 400 million herbarium specimens held across approximately 3,500 herbaria worldwide (Davis 2023). Over the past decade, many institutions have embarked on large-scale digitisation initiatives to robustly and efficiently digitise herbarium sheet collections. While these processes can be highly productive, reaching daily rates of 600–1,000 herbarium sheets in some programmes, there remains a risk and cost associated with re-digitisation, often caused by complications during the imaging stage. To address these challenges, the Natural History Museum, London (NHM) partnered with the Royal Botanic Garden Edinburgh (RBGE) to develop an Artificial Intelligence (AI)-based workflow aimed at providing enhanced quality control of herbarium sheet digitisation. This initiative was supported by the UK Government’s Department for Culture, Media and Sport (DCMS) through the NHM–DCMS AI Pilot Programme (Poon et al. 2026). The aim of this project was to develop an AI-assisted quality control framework (Zhang et al. 2025). Trained on collections at RBGE, the tool enables automated quality checks of digitised herbarium sheets. This is done by detecting missing objects (such as colour reference cards or barcodes), identifying cropping errors, and highlighting focus issues (Fig. 1). The pipeline integrates several computational techniques, including object detection, decision trees, classical statistics, and deep learning methods. In addition to post-digitisation analysis, a user-friendly interface was also developed to support in situ quality checks during the imaging stages. Model evaluation on an independent test set demonstrated the high accuracy and robustness of the AI-assisted framework (Zhang et al. 2025). The object detection model achieved an overall precision of 0.98, recall of 0.98, and mean Average Precision scores of 0.99 (mAP50) and 0.94 (mAP50–95) across all object categories. Key reference elements, including colour reference cards, barcodes, and rulers, were detected with near-perfect accuracy, ensuring precise object localisation. We note that as model development was done on RBGE collections alone, this meant that reference elements were similar across many herbarium sheets in the sample. Lastly, the issue detection models also performed strongly, achieving a precision of approximately 1.00 for out-of-focus detection, 0.98 for over-cropping, and 0.93 for under-cropping. The proposed workflow underwent further evaluation with herbarium digitisers at RBGE, which will inform refinements to the model, software interface, and the overall pipeline. The next step is to expand on this pilot project by testing the model with collections across different institutions, leading to more refinements for generalisability. This can also help us evaluate and estimate the total proportions of digitised herbarium sheets with issues. Finally, our global aim is to provide an AI-based quality control service that can be integrated into digitisation workflows not only at RBGE but across institutions worldwide.