# CentralBark Image Dataset and Tree Species Classification Using Deep Learning
## 1 Introduction  
Proper tree species identification is a crucial tool for sustainable forest management, climate change mitigation, and biodiversity conservation. By accurately identifying trees, practitioners, landowners, and the general public can better understand the current state of a forest ecosystem and implement appropriate management [1]. However, manual identification is tedious, and there are multiple methods for identifying trees through leaves, buds, fruit, and bark observation. Although leaves are commonly used for identification in the spring, summer, and early fall seasons, their reliability decreases greatly once they sensee and fall off the trees for the winter. Using fruit or flowers is limited, as once they fall, it becomes difficult to attribute them to a particular tree. Another way to identify trees is the use of buds in the spring, but buds are hard to reach for tall trees and slight differences within species may be difficult to identify from a distance [2]. By far, the most reliable and well-agreed-upon method to identify trees is by using the bark, which is accessible and does not vary too much throughout the year. Tree bark exhibits various patterns, shapes, and textures that make for a more reliable identification method [3]. However, much like the twig buds, a trained eye is required to identify trees accurately based on the bark. Foresters, botanists, and other professionals involved in forest management need to have a solid understanding of these methods to accurately identify tree species andmake informed decisions regarding forest management. By doing so, they can ensure the long-term health and sustainability of forest ecosystems. Over the past two decades, various image-processing studies have attempted to improve the accuracy of tree identification based on bark by treating it as a texture-recognition task [4]. Typically, these studies employ a two-step method, which involves extracting features from images and then feeding them into linear classifiers, e.g., Support Vector Machine, or non-linear classifiers, e.g., Multilayer Perceptron. Some of these methods include Gabor filter banks as proposed by [PERSON] et al. [5]; co-occurrence matrices; histogram and auto-correlation methods, as applied by [PERSON] et al. [6]; and the Grey-Level Co-occurrence Matrix with Long Connection Length Emphasis, as employed by [PERSON] et al. [7]. Newer studies, namely [PERSON] et al. [8], have used state-of-the-art computer-vision class activation mapping (CAM) to differentiate and classify bark patterns and further understand the nested groups of parameters in CNNs. Other studies have added color features, such as [PERSON] et al. [6], or utilized handcrafted features, such as the shape, color, structure, and orientation of bark with the help of Canny filters, hue histograms, and Gabor filters, as proposed by [PERSON] et al. [9]. [PERSON] et al. [10] introduced the Statistical Macro Binary Pattern (SMBP), a variant of the Local Binary Pattern that represents the intensity distribution within the macrostructure of large spatial support by one macro pattern code. [PERSON] [11] used Local Ternary Patterns and fed them to the Multilayer Perceptron, while [PERSON] and [PERSON] [12] introduced rotationally invariant multispectral textural features and reported 90.4% accuracy on BarkNet [3] using the nearest neighbor classifier. With the help of artificial intelligence (AI), specifically deep learning methods, and Convolutional Neural Networks (CNNs), [PERSON] et al. [13] demonstrated that accurate identification can be achieved by providing a quantitative approach to exploring unique features in each tree's bark. Deep learning is a machine learning technique that teaches computers to learn what comes naturally to humans. In this case, teaching computers to become as experienced as professional foresters is the overarching goal.