The automation of fossil pollen analysis promises many advantages in handling large
numbers of samples with less resource allocation. However, automation is often obstructed …

With the intensification of urbanization in human society, pollen allergy has become a
seasonal epidemic disease with a considerable incidence rate, seriously affecting the …

The transition from a greenhouse to an icehouse world at the Eocene-Oligocene Transition
(EOT) coincided with a large decrease of pollen from the steppe-adapted genus Nitraria …

Pollen identification is necessary for several subfields of geology, ecology, and evolutionary
biology. However, the existing methods for pollen identification are laborious, time …

Research on plant-pollinator interactions requires a diversity of perspectives and
approaches, and documenting changing pollinator-plant interactions due to declining insect …

Automatic pollen images recognition is crucial for pollinosis symptoms prevention and
treatment. The problem of pollen recognition can be efficiently solved using deep learning …

Visual classification of pollen grains is crucial for various agricultural applications,
particularly for the protection, monitoring, and tracking of flora to preserve the biome and …

Background and objective: Training a deep convolutional neural network (CNN) for
automatic image classification requires a large database with images of labeled samples …

Pollen classification is considered an important task in palynology. In the Netherlands, two
genera of the Urticaceae family, named Parietaria and Urtica, have high morphological …

The phylogenetic interpretation of pollen morphology is limited by our inability to recognize
the evolutionary history embedded in pollen features. Deep learning offers tools for …