# Tree Species Identification from Bark Images Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1803.00949 · HTML: https://mlsanity.com/paper/1803.00949/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 11 of 11)

- [Instance Segmentation for Autonomous Log Grasping in Forestry Operations](https://mlsanity.com/paper/2203.01902/) (2022)
- [AI applications in forest monitoring need remote sensing benchmark datasets](https://mlsanity.com/paper/2212.09937/) (2022)
- [Feature transforms for image data augmentation](https://mlsanity.com/paper/2201.09700/) (2022)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks](https://mlsanity.com/paper/2210.09290/) (2022)
- [Automated Feature-Specific Tree Species Identification from Natural Images using Deep Semi-Supervised Learning](https://mlsanity.com/paper/2110.03994/) (2021)
- [BarkXAI: A Lightweight Post-Hoc Explainable Method for Tree Species Classification with Quantifiable Concepts](https://mlsanity.com/paper/2502.18844/) (2025)
- [SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests](https://mlsanity.com/paper/2510.09458/) (2025)
- [Expert Knowledge-Guided Decision Calibration for Accurate Fine-Grained Tree Species Classification](https://mlsanity.com/paper/2601.16498/) (2026)
- [BarkNet-Lite: A Lightweight Texture and Colour Network with the BarkBD Benchmark for Bark-Based Tree Species Recognition in Bangladesh](https://mlsanity.com/paper/2609.07600/) (2026)
- [Retraining-Free Depth Pruning of Vision Transformers: A Cross Domain Study of Layer Redundancy](https://mlsanity.com/paper/2610.15005/) (2026)

## Abstract (arXiv)

Tree species identification using bark images is a challenging problem that could prove useful for many forestry related tasks. However, while the recent progress in deep learning showed impressive results on standard vision problems, a lack of datasets prevented its use on tree bark species classification. In this work, we present, and make publicly available, a novel dataset called BarkNet 1.0 containing more than 23,000 high-resolution bark images from 23 different tree species over a wide range of tree diameters. With it, we demonstrate the feasibility of species recognition through bark images, using deep learning. More specifically, we obtain an accuracy of 93.88% on single crop, and an accuracy of 97.81% using a majority voting approach on all of the images of a tree. We also empirically demonstrate that, for a fixed number of images, it is better to maximize the number of tree individuals in the training database, thus directing future data collection efforts.