Efficient Automatic Pollen Recognition From Fossil Pollen Samples: A High-Resolution Example Record From Palaeolake Kieshofer Moor, Northeastern Germany.

Pollen analysis is a crucial tool for reconstructing past vegetation and ecosystems. Until now, pollen analysis has been a time-consuming manual process, severely limiting the number of records an analyst can produce and their temporal resolution. Recently, automatic approaches based on artificial neural networks have shown potential for classifying multiple pollen types. These approaches performed well with clean, modern reference material, but not with real-world fossil pollen samples from, for example lake sediments. To overcome this limitation, our TOFSI approach uses two neural networks to first detect and then classify pollen and other objects. Here, we apply the approach, for the first time, to a long lake sediment sequence at a very high resolution of 1 cm. To this end, a model has been trained to recognise 48 pollen, spore and NPP classes. Our approach performs excellently for the classes that are well represented in the training data. At the 0.5 confidence level, the automatic recognition achieves a recall and precision of at least 0.9. However, performance tends to decline for classes with fewer than ~100 training objects. We conclude that, when suitable images and model training are provided, TOFSI can accurately detect and classify multiple pollen, spores and NPP classes in lake sediment samples. The approach hence allows fully automated analysis when limited taxonomic resolution is sufficient. When full taxonomic resolution is required, TOFSI can be used in a semi-automatic approach involving manual revision of critical objects. Both approaches substantially reduce analysis times, while the resulting count sums and, consequently, the statistical reliability of the results are often much higher. Besides improved productivity, an image-based workflow could offer palynologists several practical improvements, including simplified student training and communication between researchers. Extended documentation and long-term storage of results may improve the standardisation of pollen counts.