Abstract
In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models. Our proposal manages to properly classify up to 98% of the examples from a dataset with 46 different classes of pollen grains, produced by the Classifynder classification system. This is an unprecedented result which surpasses all previous attempts both in accuracy and number and difficulty of taxa under consideration, which include types previously considered as indistinguishable.
Highlights
Pollen is widely recognized as a nuisance, and as a valuable tool in several scientific fields
Pollen is important for quality verification of honey [3], reconstructing past vegetation to understand past changes in climate change [4], biodiversity [5], and human impacts [6] and as a forensic tool [7]
While other branches of science have been transformed by the technological advances of recent decades, palynology is languishing, with the practical methodology of pollen counting having hardly advanced much beyond that of the 1950s
Summary
Pollen is widely recognized as a nuisance, and as a valuable tool in several scientific fields. Pollen forecasting, informed by examination of airborne pollen, has become a key tool for management of SAR [2]. Pollen is important for quality verification of honey [3], reconstructing past vegetation to understand past changes in climate change [4], biodiversity [5], and human impacts [6] and as a forensic tool [7]. Common to all these areas is the need for experienced analysts to spend considerable amounts of time identifying and counting pollen on slides. While other branches of science have been transformed by the technological advances of recent decades, palynology is languishing, with the practical methodology of pollen counting having hardly advanced much beyond that of the 1950s
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