# CentralBark Image Dataset and Tree Species Classification Using Deep Learning
## 5 Conclusions  
We created a large and comprehensive dataset while also laying a framework for an easy and repeatable protocol that allows datasets to be created and added to. Our dataset is unique as it contains many attributes other than just the images of the tree bark, such as diameter, the moisture condition of tree bark, GPS location, images from different cardinal directions, and different light conditions. This dataset can be used for further research on bark classification for forestry and AI applications.  
We also demonstrated the capability of our dataset against three different existing CNN models against our own dataset, in performing accurate species identification from bark images of 25 Central Hardwood and Appalachian region species. Using our dataset, we achieved the following results: EfficientNet-b3 achieved an accuracy of 83.21%, ResNet50 achieved 81.37%, and MobileNet-V3-small achieved 76.11%. It is worth noting that we did not crop or alter the images in any way, and the results were achieved using the raw images.  
To overcome the limitations of this study, more work is needed to improve our current model, including the gathering of additional images. It is essential to ensure that the images in the dataset are representative of the entire population of tree species, to avoid any bias in the training of the model. We are currently collecting images in the north and south of the eastern US to include more species and regional variations. Additionally, we need to address the issue of defects, moisture conditions, and DBH, which can adversely affect classification accuracy.  
Future research can also focus on extending the scope of the dataset beyond the Central Hardwood and Central Appalachian regions to include other regions around the world. Through Purdue University's Institute for Digital Forestry, we are expanding the database to contain twice as many hardwood species and some softwoods. Species expansion and data collection have begun in Maine and Georgia to represent the northeast and southeast regions of the United States. This will enable us to identify and classify tree species across various geographic locations, thereby making a significant contribution to the conservation and management of forest resources and helping move AI further in the direction of simplifying forestry-related tasks. Overall, our study has shown the potential of using deep learning methods in bark identification and has laid the groundwork for further research in this field.  
Conceptualization, R.G. and C.W.; methodology, C.W. and F.W.; software, F.W. and N.K.; validation, C.W. and F.W.; formal analysis, F.W.; investigation, C.W. and F.W.; resources, C.W.; data curation, N.K.; writing--original draft preparation, C.W. and F.W.; writing--review and editing, R.G., B.B. and S.F.; visualization, F.W.; supervision, R.G. and S.F.; project administration, R.G.; funding acquisition, R.G. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the PERSEUS project, award # 2023-68012-38992 under USDA NIFA. The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of NIFA or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.  
The authors of this paper will release 1 image for each species found in the CentralBark Database. The remainder of the data will be withheld for the continuation of research at Purdue University's Institute for Digital Forestry.  
The authors declare no conflicts of interest.