Julie Allen

Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study

Journal Of Big Data · 2023-10-01 · 26 citations

Abstract Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approac…

A phylogenomic tree of wood-warblers (Aves: Parulidae): Dealing with good, bad, and ugly samples

Molecular Phylogenetics and Evolution · 2024-11-13 · 15 citations

Humans in the loop: Community science and machine learning synergies for overcoming herbarium digitization bottlenecks

Applications in Plant Sciences · 2024-01-01 · 13 citations

Premise: Among the slowest steps in the digitization of natural history collections is converting imaged labels into digital text. We present here a working solution to overcome this long-recognized efficiency bottleneck that leverages synergies between community science efforts and machine learning approaches. Methods: We present two new semi-automated services. The first detects and classifies typewritten, handwritten, or mixed labels from herbarium sheets. The second uses a workflow tuned for…

Deep learning for accurate classification of conifer pollen grains: enhancing species identification in palynology

Frontiers in Big Data · 2025-02-14 · 7 citations

(pine) is an important method for reconstructing historical environments, past landscapes and understanding human-environment interactions. However, distinguishing between pollen grains of conifer genera poses challenges in palynology due to their morphological similarities. To address this identification challenge, this study leverages advanced deep learning techniques, specifically transfer learning models, which are effective in identifying similarities among detailed features. We evaluated n…

Ensemble automated approaches for producing high‐quality herbarium digital records

Applications in Plant Sciences · 2024-11-05 · 7 citations

Premise: One of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields. Methods: We first showcase the development of a rule-based approach and compare outcomes with a large language model-based approach, in particular ChatGPT4. We…