  Identification of tree species plays a key role in forestry related tasks
like forest conservation, disease diagnosis and plant production. There had
been a debate regarding the part of the tree to be used for differentiation,
whether it should be leaves, fruits, flowers or bark. Studies have proven that
bark is of utmost importance as it will be present despite seasonal variations
and provides a characteristic identity to a tree by variations in the
structure. In this paper, a deep learning based approach is presented by
leveraging the method of computer vision to classify 50 tree species, on the
basis of bark texture using the BarkVN-50 dataset. This is the maximum number
of trees being considered for bark classification till now. A convolutional
neural network(CNN), ResNet101 has been implemented using transfer-learning
based technique of fine tuning to maximise the model performance. The model
produced an overall accuracy of >94% during the evaluation. The performance
validation has been done using K-Fold Cross Validation and by testing on unseen
data collected from the Internet, this proved the model's generalization
capability for real-world uses.
