Automated tree ring detection of common Indiana hardwood species through deep learning: Introducing a new dataset of annotated images

Abstract

Tree-ring dating enables gathering necessary knowledge about trees, and it is essential in many areas, including forest management and the timber industry. Tree-ring dating can be conducted on either wood’s clean cross-sections or tree trunks’ rough end cross-sections. However, the measurement process is still time-consuming and frequently requires experts who use special devices, such as stereoscopes. Modern approaches based on image processing using deep learning have been successfully applied in many areas, and they can succeed in recognizing tree rings. While supervised deep learning-based methods often produce excellent results, they also depend on extensive datasets of tediously annotated data. To our knowledge, there are only a few publicly available ring image datasets with annotations. We introduce a new carefully captured dataset of images of hardwood species automatically annotated for tree ring detection. We capture each wood cookie twice, once in the rough form, similar to industrial settings, and then after careful cleaning, that reveals all growth rings. We carefully overlap the images and use them for an automatic ring annotation in the rough data. We then use the Feature Pyramid Network with Resnet encoder that obtains an overall pixel-level area under the curve score of 85.72% and ring level F1 score of 0.7348. The data and code are available at https://github.com/wufanyou/growth-ring-detection.

Keywords

• Dendrochronology
• Deep learning
• Semantic segmentation