Tree Species Identification from Bark Images Using Convolutional Neural Networks

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.

I. Introduction

The ability to automatically and reliably identify tree species from images of bark is an important problem, but has received limited attention in the vision and robotics communities. Early work in mobile robotics has already shown that the ability to recognize trees from non-trees in combined LiDAR+camera sensing can improve localization robustness [1]. More recent work on data-efficient semantic localization and mapping algorithms [2], [3] have demonstrated the value of semantically-meaningful landmarks; In our situation, trees and the knowledge of their species would act as such semantic landmarks. The robotics community is also increasingly interested in flying drones in forests [4]. In terms of forestry applications, one could use this visual species identification to perform autonomous forest inventory. In the context of autonomous tree harvesting operations [5], the harvester or forwarder would be able to sort timber by species, improving the operator's margins. Similarly, sawmill processes such as debarking could be fine-tuned or optimized based on the species knowledge of the currently processed log.