Abstract
In a large number of scientific areas, such as immunology, forensics, paleoecology, and archeology, the study of pollen, i.e., palynology, plays an important role: from tracking climate changes, studying allergies, to forensic investigations or honey origin analysis. Since the mid-nineties of the last century, the idea for an automated solution to the problem of pollen identification and classification was formulated and since then, several attempts and proposals have been made and presented, based on different technologies, in particular in the field of Computer Vision. However, as of 2021 microscopic analyses are performed mainly manually by highly trained specialists, although the capabilities of artificial intelligence, especially Deep Neural Networks, are steadily increasing. In this work, we analyzed various state-of-the-art research work concerning pollen detection and classification and compared their methods and results. The problems, such as data accessibility, different methods of Machine Learning, and the intended applicability of the proposed solutions are explored. We also identified crucial issues that require further work and research. Our work will provide a thorough view on the current state of the art, its issues, and possibilities for the future.
Highlights
In the area of Computer Vision (CV) and Pattern Recognition, the classification of microscopic images is a broad topic with a large number of possible applications in various fields
Proposed pollen classification solutions should be evaluated on uniform data sets that are accessible to the scientific community
We analyzed a large number of CV-based pollen classification methods in the area of Machine Learning (ML) and Deep Learning (DL)
Summary
In the area of Computer Vision (CV) and Pattern Recognition, the classification of microscopic images is a broad topic with a large number of possible applications in various fields. The advent of Machine Learning (ML) and especially Deep Learning (DL) is major drivers in this area, combined with the steadily increasing computational power, leveraging microscopic pollen analysis. The advantages of automated and Artificial Intelligence (AI)-based pollen detection and classification is manifold: In all areas of applications, it can reduce costs, expenditure of time, and increase accuracy. A successful deployment of a pollen classification system is based on three important factors, of which two are mandatory: the software, i.e., the method of pollen detection and classification, the data, and the hardware. The question of how to acquire quality images of pollen grains outside of a laboratory has to be considered as well. The process of pollen acquisition requires additional steps, e.g., creating a sediment from a honey sample or capturing airborne pollen; the entire system becomes more complex
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