Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks

TL;DR

This paper presents a deep learning approach using ResNet101 CNN to classify 50 tree species based on bark texture, achieving over 94% accuracy and demonstrating strong generalization for forestry applications.

Contribution

It introduces the largest bark texture dataset for classification and applies transfer learning with ResNet101 to improve accuracy in tree species identification.

Findings

Achieved over 94% accuracy in bark-based tree classification

Validated model's generalization with unseen internet data

Demonstrated effectiveness of transfer learning for forestry tasks

Abstract

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.