Abstract:

Objective: An image recognition method based on convolutional neural networks(CNNs) for endangered and precious tree species was developed in this study to reach the automatic and accurate identification of timbers at the species level in multi-scene scenarios. Method: Images representing wood anatomical features were collected from transverse section of authentic wood specimens for 15 Dalbergia and 11 Pterocarpus species, and an image data set of Rosewood-26 was established. Four CNN models, i.e. AlexNet, VGG16, DenseNet-121 and ResNet-50 were constructed and pre-trained with ImageNet for transfer learning. And then the image data set of Rosewood-26 was deployed to re-train and test these models, which were comparatively analyzed and evaluated to obatin an optimal one for wood identification. A wood identification system was developed for identifying those samples collected from timber market for application test. Result: Among the four CNN models, ResNet-50 showed the highest identification accuracy(98.33%) and lower model complexity, which is preferable in the context of accurate and rapid wood identification. The ResNet-50 model achieved 100% accuracy when identifying 9 Dalbergia and 3 Pterocarpus species, and successfully discriminated P. santalinus from its look-alike species, P. tinctorius. The automated wood identification system based on ResNet-50 model exhibited an identification accuracy of 91.8% at the genus level and 77.3% at the species level. Conclusion: The wood identification system, iWood, developed in this study using CNNs is applicable in broad fields, i.e. customs enforcement, timber trade and quality inspection, and can reach automatic and accurate identification of wood species. This study will provide scientific support for promoting regulation of forest product industry chain, enhancing CITES enforcement capabilities and protecting forest species diversity.

Key words: iWood, convolutional neural networks, wood anatomical features, image dataset, automated identification, identification accuracy