# A Deep Learning-Based Model for Tree Species Identification Using Pollen Grain Images
## 5 Conclusions and Future Work  
The objective of this study was to develop a deep learning-based tree species identification model using pollen grain images taken with a camera mounted on an optical microscope. Because pollen grains are roughly spherical, we evaluated the tree species classification accuracy of deep learning algorithms using images of the same pollen grains taken with different focal points. Although classification accuracy varied among tree species and it was difficult to identify all tree species without error, the best model showed an MCC value > 0.9 and was therefore found to have high accuracy. Future studies should evaluate learning models using pollen grains from more tree species and sedimentary fossil pollen and consider the application of other deep learning models. In this study, we verified the classification accuracy when the pollen analyst performs identification using various focal points, assuming that the material is actually used for pollen analysis. Since we focused on whether the materials used in this study could be identified, the deep learning model has not been fully verified. Currently, there are powerful networks, such as ResNet [44] and DenseNet [45], that have more layers than AlexNet or GoogLeNet. Generally, deep learning tends to upgrade image classification performance as the number of layers increases [13]. In addition, deep learning algorithms with many layers need a huge amount of computation as the number of layers increases, which causes long training times [43]. In addition, problems such as overfitting [42] and vanishing gradient problem [46] are also known. Therefore, it is preferable to consider the optimal type of learning model for different problem sets, rather than simply using a model with many layers. However, tree species identification was possible using deep learning from actual pollen data used in the pollen analysis. We believe that future validation using more powerful networks such as ResNet and DenseNet is necessary. Supplementary Materials:The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122412626/s1](https://www.mdpi.com/article/10.3390/app122412626/s1)]([https://www.mdpi.com/article/10.3390/app122412626/s1](https://www.mdpi.com/article/10.3390/app122412626/s1)), Table S1: Tree species classification accuracy in terms of the F score for various focal points. Single and double underlining indicates minimum and maximum values, respectively, in terms of the number of epochs. Conceptualization, Y.M. ; methodology, Y.M. and H.T. ; software, Y.M. ; validation, Y.M., K.S. and H.T. ; formal analysis, Y.M. and K.S. ; investigation, K.S. ; resources, K.S. and H.T. ; data curation, Y.M., K.S. and H.T. ; writing--original draft preparation, Y.M. and K.S. ; writing--review and editing, Y.M. and H.T. ; visualization, Y.M. ; supervision, Y.M. ; project administration, Y.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. We thank the Forest Vegetation Dynamics Laboratory, Kyoto Prefectural University, and the collectors of specimens held in the laboratory, for allowing us to use their pollen samples in this study. Four anonymous referees provided valuable comments on earlier drafts of the manuscript. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: