# CentralBark Image Dataset and Tree Species Classification Using Deep Learning
## 3 Results  
We have demonstrated the capability of EfficientNet-b3 to perform accurate species identification from bark images of 25 Central Hardwood and Appalachian region species. Using our dataset, we achieved 83.21% overall accuracy with the EfficientNet-b3 model. EfficientNet-b3 has several architectural advantages compared to ResNet and MobileNet-v2 that likely contribute to its superior performance on many tasks. In addition to the success of the CNN model, we also created a large and comprehensive dataset named CentralBark, with over 19,000 images. This dataset can be used to further the research on bark classification for forestry and natural resources and AI learning applications, as there exists an urgent need for large, standardized bark image datasets for tree bark classification. Table 2 shows each model's performance by species from our dataset. We used micro-average accuracy, which treats each sample as the same weight when averaged. The EfficientNet-b3 architecture achieved the highest accuracy with a score of 83.21%. The second-best performing model was ResNet-50, with an accuracy score of 81.37%, followed by MobileNet-V3-small, with a score of 76.11%. It is worth noting that the performance order of these models is consistent with their performance on the ImageNet dataset [22]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Common Name**} & **EfficientNet-b3** & **ResNet-50** & **MobileNet-V3-Small** \\  & & **(Accuracy \%)** & **(Accuracy \%)** & **(Accuracy \%)** \\ \hline _Tilia americana_ & American basswood & 65.72 & 53.00 & 44.88 \\ \hline _Fagus grandifolia_ & American beech & 96.02 & 93.11 & 95.67 \\ \hline _Ulmus americana_ & American elm & 69.53 & 70.47 & 68.59 \\ \hline _Platanus occidentalis_ & American sycamore & 90.52 & 92.35 & 86.75 \\ \hline _Cary cordinornis_ & Bitternut hickory & 63.38 & 68.97 & 55.17 \\ \hline _Pruus americana_ & Black cherry & 75.70 & 74.17 & 71.44 \\ \hline _Robinia pseudocacia_ & Black locust & 89.22 & 88.91 & 86.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CentralBark dataset performance vs. all three models (EfficientNet-b3, ResNet-50, and MobileNet-V3-small). The confusion matrix in Figure 3 reveals that some tree species are incorrectly identified by the EfficientNet-b3 model, resulting in false positives. This inaccuracy can be attributed to three main factors: the diameter at breast height (DBH) or the age of the tree, the color and physical appearance of the bark, and subtle differences within species. For instance, when trees are smaller in diameter, species like sugar maple (_Acer saccharum_) and hackberry (_Celtis occidentalis_) have very similar bark. Hackberry can be identified by its smooth bark when young, but it soon develops warty growths as the tree matures [23]. Consequently, when young, sugar maps appear smooth, and then, as they mature, the ridges and furrows become more pronounced. Because of the similarities between the two species, we ran into inaccuracies in our confusion matrix. However, we found that with the increasing diameter (age), the models were able to differentiate between them with greater results. Similarly, black cherry (_Prunus serotina_) and white oak (_Quercus alba_) can be confused because of their shared blocky and flaky bark pattern, despite their different morphology. Also, the bark of cherry tends to bleach when exposed to sunlight, changing its shade from a darker gray to a lighter one, which can further complicate identification. Moreover, the differences in bark appearance within species are another significant source of confusion, as observed in species such as American elm (_Ulmus americana_) vs. slippery elm (_Ulmus rubra_) or pignut hickory (_Carya glabra_) vs. bitternut hickory (_Carya coriformis_). There are often minute differences in the bark pattern between species of the same genus, such as the interlacing pattern in the tight bark of pignut and bitternut hickory. Professional foresters identify these species based on touch.