Tree Species Identification from Bark Images Using Convolutional Neural Networks

By Mathieu Carpentier, Philippe Giguère, Jonathan Gaudreault. 2018

72 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Nature and Landscape Conservation, Organic Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jean-Michel Fortin
2. Vincent Grondin
3. Olivier Gamache
4. Philippe Giguère
5. François Pomerleau

In Environmental Engineering

1. Philippe Giguère
2. François Pomerleau

Citations in the corpus

• Instance Segmentation for Autonomous Log Grasping in Forestry Operations (2022, background): “[24] released a large bark images dataset for tree species identification.”