Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks
This work addresses the need for automated tree species identification in forestry tasks like conservation and disease diagnosis, offering a practical solution that is robust to seasonal variations, though it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of identifying tree species by classifying bark textures using a convolutional neural network, achieving an overall accuracy of over 94% on 50 species, which is the largest number considered for bark classification to date.
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.