# A Deep Learning-Based Model for Tree Species Identification Using Pollen Grain Images
## 4 Discussion
However, focal point C tended to show lower classification accuracy than all other focal points; therefore, in accumulating a new pollen grain image dataset, it is appropriate to create images at focal point A or E, because these capture the pollen surface structure in a manner associated with high classification accuracy, and to apply a learning model created using images taken at focal point C to identify pollen grain images with unknown focal points. In our previous studies on tree species identification by deep learning based on leaf images, GoogLeNet tended to show higher classification accuracy for most training patterns, regardless of whether data augmentation was applied [25; 39; 40]. In this study, GoogLeNet tended to have higher classification accuracy than AlexNet for many models. Due to similar results in tree species identification based on leaf images [12; 41], GoogLeNet is regarded as one of the most effective deep learning algorithms. Since GoogLeNet has more layers than AlexNet, we consider that GoogLeNet can achieve higher discrimination accuracy. Many deep learning algorithms tend to improve classification performance when the number of layers is increased [13]. In this study, AlexNet showed higher classification accuracy than GoogLeNet in many cases, especially in learning without data augmentation. Progress has been made in developing deep learning algorithms with more convolutional and pooling layers, and models with more layers often have higher classification performance. Each deep learning algorithm has different constraints (image type and image size) on the input data for each algorithm. In particular, as the number of layers is increased, factors such as the method of combining layers, selection of activation functions to be applied, and adjustment of hyperparameters become more complex. Because it is not always clear which learning algorithm should be applied to the object to be identified in deep learning, it is necessary to consider various learning algorithms, and it is not always sufficient to select a model with a large number of layers. Furthermore, in many deep learning models, the learning time increases as the layer number increases, such that running various patterns (e.g., in this study, we varied the focal point and epoch number) can require enormous amounts of effort and time. Therefore, it is preferable to use a training model with as short a training time as possible and with high classification performance. As the AlexNet algorithm can be trained in less than half of the time required by GoogLeNet under the same training environment, we expect that AlexNet will be sufficient without data augmentation. The classification accuracy of the training data also increased as the number of epochs increased, reaching almost 100% for most of the training models at an epoch number of 200. However, excessive learning can result in overfitting, which reduces classification accuracy [42]. In this study, when the epoch number was increased from 50 to 100, classification accuracy was greatly improved in some models, whereas when the epoch number was increased from 100 to 200, classification accuracy decreased slightly in some models, which may indicate overfitting. As shown in Table S1, the classification accuracy results were generally similar to the tendency of misclassification patterns but differed slightly according to focal points. In this study, C.cr., F.s., and Q.c. showed the highest classification accuracy, whereas C.co., F.j., G.i., Q.ser., Q.ses., and C.t. showed the lowest classification accuracy. The results shown in Figure 3 (without data augmentation, focal point D, AlexNet, 200 epochs) were generally consistent with these results, although the trend varied depending on the learning model; A.j., Cam.j., C.cr., F.s., Q.c., and C.si. showed the highest classification accuracy, whereas C.co., F.j., G.i., and Q.ses. showed the lowest classification accuracy.