Pattern recognition methodologies for pollen grain image classification: a survey

Abstract In a large number of scientific areas, such as immunology, forensics, paleoecology, and archeology, the study pollen, i.e., palynology, plays an important role: from tracking climate changes, studying allergies, to forensic investigations or honey origin analysis. Since mid-nineties last century, idea for automated solution problem pollen identification classification was formulated since then, several attempts proposals have been made presented, based on different technologies, in particular field Computer Vision. However, 2021 microscopic analyses are performed mainly manually by highly trained specialists, although capabilities artificial intelligence, especially Deep Neural Networks, steadily increasing. this work, we analyzed various state-of-the-art research work concerning detection compared their methods results. The problems, data accessibility, Machine Learning, intended applicability proposed solutions explored. We also identified crucial issues that require further research. Our will provide thorough view current state art, its issues, possibilities future.