# A Deep Learning-Based Model for Tree Species Identification Using Pollen Grain Images
### Misclassification Patterns according to Tree Species and Pollen Type  
Based on the results presented in Table 2, we created heatmaps of the highest and lowest MCC values to visualize misclassification patterns according to tree species and pollen type, for models run with and without data augmentation, based on a contingency table of classification results for 10 simulations (Figures 3 and 4). Figure 3 shows that all Q.c. and C.si. specimens were correctly classified, and C.cr. (1 total misclassification) and F.s. (2) specimens were almost all correctly classified. By contrast, species such as C.co. (19), F.c. (19), G.i. (18), Q.ser. (15), and Q.ses. (17) showed higher misclassification rates. The most commonly misclassified species were C.co. (9), Q.ser. (7), and F.c., G.i., and Q.ses. (5 each). Other tree species that were commonly misclassified as true positives were Q.ses. (8), Q.ser. (7), and A.j. and B.p. (6 each). We also found that F.c. (0) and C.cu. and J.m. (1 each) had low classification accuracy but were not frequently misclassified as positives. Among the most common misclassifications were Cam.j. to F.j. (8), G.i. to A.j. (8), Q.ses. to Q.ser. (8), and Z.s. to U.d. (8). Figure 4 shows that A.j., Cam.j., and Carj. were all correctly classified, and C.cr. (4), J.m. (2), and U.d. (1) were nearly always correctly classified. By contrast, species such as C.co. (125), F.j. (98), and Q.ses. (85) showed higher misclassification results. The most commonly misclassified species were C.co. and Q.ses. (7), C.t. (6), and F.j. and P.a. (5). Other tree species that were commonly misclassified as positives were A.j. and U.d. (7), Q.ses. (6), and P.a., Q.ser., and Carj. (5 each). By contrast, although F.c., F.j., F.s., G.i., Q.c., and C.t. (1 each) had low classification accuracy, they were not frequently misclassified as positives. Among the most common misclassifications were C.co. to P.a. (71), F.j. for Cam.j. (61), Q.ses. for Q.ser. (42), and C.co. for Q.ser. (8). Table 4 shows misclassification rates according to pollen type, normalized by the number of trees per pollen type, based on the results shown in Figures 3 and 4. Based on the results shown in Figure 3 (without data augmentation, focal point D, AlexNet, 200 epochs), tricoloporate and stephanoporate pollen grains were the pollen types least likely to be misidentified, whereas triporate pollen was the most frequently misidentified pollen type, and most often as tricoloporate pollen. The tree species that were not misclassified when misclassified to different pollen types were Carj. (triporate) and C.t. (stephanoporate) in the case of tricoloporate, 10 species (tricoloporate, except for Q.c., Q.ser., and Q.ses.) and 3 species (stephanoporate, except for U.d.) in the case of triporate, and 8 species (tricoloporate, except for C.cr., F.j., P.a., Q.ser., and Q.ses.) in the case of stephanoporate. Figure 4: Heatmap of misclassification patterns according to tree species and pollen type. Results were obtained using the GoogLeNet algorithm with data augmentation using focal point E and 200 epochs. Values are the sums of 10 simulation results. Underlining indicates the 10 most frequent cases. 1 Number of tree species misclassified, 2 number of times a species was misclassified as a positive example of another tree species, 3 pollen types (Morita [26]). Figure 3: Heatmap of misclassification patterns according to tree species and pollen type. Results were obtained using the AlexNet algorithm without data augmentation using focal point D and 200 epochs. Values are the sums of 10 simulation results. Underlining indicates the 10 most frequent cases. 1 Number of tree species misclassified, 2 number of times a species was misclassified as a positive example of another tree species, 3 pollen types (Morita [26]).