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Measuring tree stem diameters and straightness with depth-image computer vision

Journal of Forestry Research 2023年第5期34卷 1395-1405页

作者： Hoang Tran Keith Woeste Bowen Li Akshat Verma Guofan Shao Department of Computer Science Purdue University305 N University StWest LafayetteIN 47907USA Hardwood Tree Improvement and Regeneration Center USDA Forest ServiceNorthern Research Station715 West State StreetWest LafayetteIN 47907USA Department of Forestry and Natural Resources Purdue University715 West State StreetWest LafayetteIN 47907USA School of Electrical and Computer Engineering Purdue University610 Purdue MallWest LafayetteIN 47907USA

Current techniques of forest inventory rely on manual measurements and are slow and labor *** developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor *** developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem *** use,mean absolute error(MAE),and root mean squared error(RMSE)metrics were used to compare the system against manual methods,and to compare the system against itself(reproducibility).Depth-derived diameter measurements of the stems at the heights of 0.3,1.4,and 2.7 m achieved RMSE of 1.7,1.5,and 2.7 cm,*** ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained *** ForSense system was also consistent,achieving sub-centimeter diameter difference with subsequent measures and less than 4%difference in straightness value between *** method of forest inventory,which is based on depth-image computer vision,is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion(SFM)photogrammetry and ground-based LiDAR or terrestrial laser scanning(TLS).

关键词： Forest inventory Depth sensing Computer vision Tree diameter stem straightness Trunk volume

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