Species identification is one of the key steps in the management and conservation planning of many forest ecosystems. We introduce Deep BarkID, a portable tree identification system that detects tree species from bark images. Existing bark identification systems rely heavily on massive computing power access, which may be scarce in many locations. Our approach is deployed as a smartphone application that does not require any connection to a database. Its intended use is in a forest, where internet connection is often unavailable. The tree bark identification is expressed as a bark image classification task, and it is implemented as a convolutional neural network (CNN). This research focuses on developing light-weight CNN models through knowledge distillation. Overall, we achieved 96.12% accuracy for tree species classification tasks for ten common tree species in Indiana, USA. We also captured and prepared thousands of bark images—a dataset that we call Indiana Bark Dataset—and we make it available at https://github.com/wufanyou/DBID.

物种鉴定是许多森林生态系统管理和保护规划的关键步骤之一。我们介绍Deep BarkID，一种便携式树木识别系统，可从树皮图像中检测树种。现有的树皮识别系统严重依赖海量计算能力访问，这在许多地方可能是稀缺的。我们的方法部署为不需要任何数据库连接的智能手机应用程序。它的预期用途是在经常无法连接互联网的森林中。树皮识别被表示为树皮图像分类任务，它被实现为卷积神经网络（CNN）。本研究侧重于通过知识蒸馏开发轻量级 CNN 模型。总体而言，我们在美国印第安纳州十种常见树种的树种分类任务中实现了 96.12% 的准确率。