An Automated Method For Stem Diameter Measurement Based on Laser Module and Deep Learning

Background: Stem diameter (SD) measuring is a crucial foundation for forest resource management. SD measuring in various scenarios necessitates the involvement of expert personnel. As a result, the adoption of automatic measurement methods is greatly expected. In this study, we built a device with an integrated image sensor and laser module to measure the SD of trees of varied diameters. The spot generated by the laser module on the tree stem can be utilized as reference information for measuring height and SD with this device. An end-to-end model is performed to identify the trunk in the panchromatic image, then SD is calculated from the linear relationship between the trunk contour and the spot diameter in pixels. Results: : We measured in three scenarios with different land cover types, i.e., transitional woodland/shrub, mixed forest, and green urban area. The overall SD ranges from 2.00 cm to 89.00 cm. To investigate the differences in measurements in different natural scenarios, we analyzed the accuracy of the device for measuring trees of different diameters. The results showed a high consistency between our method and the tape measure measurements. The absolute mean error was 0.36 cm and the root mean square error was 0.45 cm. Compared to most studies, our method demonstrates better versatility and exhibits higher performance. Our integrated device is low cost, portable, and without the assistance of a tripod. Conclusion: Our method achieves the objective of automated measurements and is experimentally proven to possess the potential of efficient and accurate measurement of SD in natural scenarios. Although some limitations remain at present, the device can be employed as a means of forest inventory. In the future, the device can be further explored to be integrated into autonomous mobile robots for more scenarios.