## 3 Materials and Methods  
### Dataset  
Over recent years, deep learning has enabled significant progress to be made in the fields of image classification [25, 26, 27, 28] and feature learning [29, 30, 31, 32]. One of the key drivers of the success of deep learning is the availability of large amounts of annotated data. Indeed, the amount of training data has a great impact on the performance of deep learning processes, which encourages the development of large-scale datasets in different scientific fields [33]. Due to regional variations in plant species, there are a great number of differences in the types and shapes of pollen grains from different regions. Therefore, the collection of pollen grains from specific areas is of key significance for pollen forecasting in those areas. Section 2 introduces several open-access datasets of pollen grains from different areas. However, the categories of pollen in these datasets do not cover all of the categories that exist in Beijing, China. From a survey of plants in Beijing, the main airborne allergenic pollens were found to include a total of eight taxa: Pinaceae, Cupressaceae, Salicaceae (including _Salix_ and _Populus_), Moraceae, _Artemisia_, Compositae, Chenopodiaceae and Poaceae. These taxa cover about 90% of all pollen grains in the Beijing area. Out of these eight taxa, Cupressaceae, Salicaceae (including _Salix_ and _Populus_) and Pinaceae are the main pollen contributors in spring, while Moraceae, Compositae and Chenopodiaceae are the main pollen contributors in autumn. However, there is not a large-scale, high-quality and adaptable dataset of pollen from Beijing, which limits the automatic classification and prediction of pollen in the Beijing area. Therefore, we constructed a dataset of pollen grains that were collected in the Beijing area for the automatic classification task. The Beijing Meteorological Service Center collected pollen from 12 administrative regions in Beijing using glass slides that were coated with Vaseline. After treatment with basic fuchesin, the pollen grains on the slides became pink. Then, the slides were scanned using a NanoZomer Digital Pathology Slide Scanner (NDP). Using a 40x objective lens, we obtained pollen scan images in NDPI format and each image was approximately 100,000 \(\times\) 50,000 pixels. We called the obtained images pollen scanning images. The obtained scanning images not only contained irregularly distributed pollen grains, but also significant background noise (i.e., debris, dust, fungal spores and air bubbles). By adding the special stain to the slides, the pollen grains were stained pink in general, while the background noise was not, as shown in Figure 1. Therefore, the color extraction method [33] could be used to extract single pollen grains, which replaced the traditional manual localization and labeling process. Due to the large size of the pollen scanning images, it was inconvenient to use the color extraction method. Thus, each pollen scanning image needed to be cropped into several rectangular images with a size of 512 \(\times\) 512 pixels. The subsequent image processing pipeline was performed on these rectangular images. The collection pipeline of our pollen grain dataset could be divided into two steps: pre-processing and labeling. The specific operation details are described in the following subsections. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Dataset**} & **Number of** & **Image** & \multirow{2}{*}{**Resolution**} & **Number of** & \multirow{2}{*}{**Region**} \\  & **Grain Images** & **Type** & & **Categories** & \\ \hline Duller’s Pollen & & & & & \\ Dataset [20] & 630 & & & & \\ POLEN23E [21] & 805 & & & & \\ Pollen13K [22] & 13,310 & & & & \\  & & & & 88.6\% of the images have an average & & \\ Pollen73S [23] & 2523 & & & & \\  & & & & 11.4\% are larger & & \\ Proposed & & & & & \\ Dataset & 4389 & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: The comparison between the proposed dataset and the main datasets that are used for the pollen image classification task.