A Deep Learning-Based Model for Tree Species Identification Using Pollen Grain Images

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. From five focal points, we took photographs of pollen collected from tree species widely distributed in the Japanese archipelago, and we used these to produce pollen images. We used Caffe as the deep learning framework and AlexNet and GoogLeNet as the deep learning algorithms. We constructed four learning models that combined two learning patterns, one for focal point images with data augmentation, for which the training and test data were the same, and the other without data augmentation, for which they were not the same. The performance of the proposed model was evaluated according to the MCC and F score. The most accurate classification model was based on the GoogLeNet algorithm, with data augmentation after 200 epochs. Tree species identification accuracy varied depending on the focal point, even for the same pollen grain, and images focusing on the pollen surface tended to be more accurately classified than those focusing on the pollen outline and membrane structure. Castanea crenata, Fraxinus sieboldiana, and Quercus crispula pollen grains were classified with the highest accuracy, whereas Gamblea innovans, Carpinus tschonoskii, Cornus controversa, Fagus japonica, Quercus serrata, and Quercus sessilifolia showed the lowest classification accuracy. Future studies should consider application to fossil pollen in sediments and state-of-the-art deep learning algorithms.