# CentralBark Image Dataset and Tree Species Classification Using Deep Learning
## 4 Discussion  
We found that EfficientNet-b3 has several architectural advantages compared to ResNet and MobileNet-v2 that likely contribute to its superior performance on many tasks. First, EfficientNet uses a compound scaling method that jointly scales the network width, depth, and resolution, allowing for a more optimal allocation of resources. This balanced scaling approach enables EfficientNet to achieve better accuracy and efficiency. Additionally, EfficientNet employs MBConv (Mobile Inverted Bottleneck Conv) blocks, which are a more efficient version of the bottleneck blocks used in ResNet. MBConv blocks use depth-wise separable convolutions and squeeze-and-excitation (SE) modules, which help improve the network's representational power while keeping the computational cost low. EfficientNet-b3 also has a larger receptive field compared to ResNet and MobileNet-v2 due to its deeper architecture and the use of larger kernel sizes in the early layers. This allows the network to capture more contextual information, which can be beneficial for cer  
Figure 3: Confusion Matrix showing the results of EfficientNet-b3 model. The Y-axis shows the true label of each species, while the X-axis exhibits the predicted label that the model produced.  
tain tasks. Finally, EfficientNet is designed to maximize accuracy for a given computational budget, allowing it to achieve higher accuracy with fewer parameters compared to ResNet and MobileNet, making them more parameter-efficient.  
One of the limitations we face with our dataset is that some of the images we collected may not have bark texture throughout the entire area, as shown in Figure 4 in a sample image of black oak (_Quercus velutina_). Additionally, there are cases where a single image may contain multiple trees, making it difficult to isolate the bark texture of the target tree. These limitations can potentially impact the performance of our model if we simply perform center cropping and pass the processed image to the CNN classifier. The classifier may become confused in such extreme cases, leading to inaccurate classification results.  
Furthermore, Figure 5 shows the relationship between the sample data size and the classification accuracy. Species with larger amounts of data generally perform better with each of the three models we used in our experiments. In our dataset we were able to capture ample amounts of images for species that are generalists, ones that grow in many different types of habitats, making searching for them relatively easy. Species that have lower classification accuracy such as sweetgum do not have nearly as much sample data as  
Figure 4: Sample image of each species that is found in the CentralBark dataset.  
they require a very specific type of habitat or location making them harder to find in our region of interest.  
Another challenge we faced in our dataset was that there was a weak correlation between model accuracy and DBH in Figure 6. In theory, there should be a strong positive correlation between accuracy and larger-diameter trees because as the trees grow larger, they begin to form unique characteristics that make them easier to identify. With that stated, we believe that if we had a threshold DBH of 14 inches rather than 8 inches, then we would see an increase in model performance across the board.  
Figure 5: Frequency vs. Accuracy. The plot shows the relationship between accuracy and image sample size for each species. The Pearson correlation (R-value) is 0.756, which indicates a strong positive relationship between them indicating that a larger sample size will increase the accuracy for each species.  
Figure 6: This plot shows the relationship between accuracy and DBH. The Pearson correlation (R value) is 0.42 which indicates a weak correlation between them.