This paper examines the potential of using few-shot learning and computer vision
techniques for detecting, identifying, and counting agricultural pests and diseases in images …

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

Airborne pollen identification is crucial to help patients prevent pollinosis symptoms. Existing
data-driven methods rely on large-scale pollen images with simple backgrounds. In real …

Although Alternaria spores are well-known allergenic fungal spores, automatic bioaerosol
recognition systems have not been trained to recognize these particles until now. Here we …

Automatic pollen detection based on light microscope (LM) images is helpful for pollinosis
symptoms prevention. Recently, many deep learning methods have been proposed to …

In biological image recognition, different species might look similar resulting in a small
margin, which causes errors in labeling images. Pollen grain image classification heavily …

Simple Summary Pollen allergy is a highly prevalent disease affecting humans worldwide.
Early pollen identification can help allergic individuals to prevent pollinosis. Recently …

Automated pollen analysis is not yet efficient on environmental samples containing many
pollen taxa and debris, which are typical in most pollen‐based studies. Contrary to …

Pollen-induced allergies affect a significant part of the population in developed countries.
Current palynological analysis in Europe is a slow and laborious process which provides …

Climate change, coupled with the rise in pollen concentrations and extended pollen
seasons, poses significant challenges to public health, particularly for individuals with pollen …