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Deep learning approaches have been adopted in Forestry research including tree classification and inventory prediction. In this study, we proposed an application of a deep learning approach, Temporal Convolution Network, on sequences of radial resistograph profiles to identify non-thrive trees and to predict wood density. Non-destructive resistance drilling measurements on South and West orientations of 274 trees in a 41-year-old Douglas-fir stand in Marion County, Oregon, USA were used as input series. Non-thrive trees were defined based on their changes in social status since establishment. Wood density was derived by X-ray densitometry from cores obtained by increment borers. Data was split for cross validation. Optimal models were fine-tuned with training and validation datasets, then run with test datasets for model evaluation metrics. Results confirmed that the application of the Temporal Convolution Network on resistograph profiles enables non-thrive tree identification with the probability, represented by the area under the Receiver Operator Characteristic curve, equal to 0.823. Temporal Convolution Network for wood density prediction showed a slight improvement in accuracy (RMSE = 18.22) compared to the traditional linear (RMSE = 20.15) and non-linear (RMSE = 20.33) regression methods. We suggest that the use of machine learning algorithms can be a promising methodology for the analysis of sequential data from non-destructive devices.
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Determination of ring-level dynamic modulus of elasticity in loblolly pine from measurements of ultrasonic velocity and specific gravity
Abstract Wood stiffness (modulus of elasticity, MOE) is an important property for conifer wood, with the variability in MOE largely being a function of both the specific gravity (SG) (wood density) and the angle of the microfibrils within the S2 layer of longitudinal tracheids. Rapid analysis techniques can be used together to quantify MOE; while SG can be determined with relative ease, this is not the case for microfibril angle, requiring expensive X-ray diffraction equipment. An alternative to microfibril angle is to measure longitudinal acoustic velocity. The objective of this study was to measure and then model the within tree variation in dynamic MOE (MOEdyn) by developing the methodology to measure ultrasonic velocity (USV) in radial samples from pith to bark using ultrasonic frequencies (>20 kHz). A total of 419 pith-to-bark radial strips, collected from multiple height levels in 92 loblolly pine (Pinus taeda) trees, were processed to obtain matching SG (2mm longitudinal) and USV (8.2-mm longitudinal) samples. Ring-by-ring SG was measured using X-ray densitometry and time-of-flight USV was measured at a 10-mm radial resolution from pith to bark. A subset of samples was sent to SilviScan to determine microfibril angle using X-ray diffraction. The relationship between microfibril angle and USV was strong (R2 = 0.91, RMSE = 2.6°). Nonlinear mixed-effects models were then developed to predict radial variation in SG, USV and MOEdyn. Fixed effects for the models, which included cambial age and height of disk within tree, had pseudo R2 values of 0.67 for SG (RMSE = 0.051), 0.71 for USV (RMSE = 316 m/s) and 0.69 for MOEdyn (RMSE = 1.9 GPa). When combined with SG measurements from X-ray densitometry, USV measurements from pith to bark are a powerful tool for assessing variability in wood stiffness.
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- Award ID(s):
- 1916720
- PAR ID:
- 10398228
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Forestry
- Volume:
- 96
- Issue:
- 4
- ISSN:
- 0015-752X
- Format(s):
- Medium: X Size: p. 588-604
- Size(s):
- p. 588-604
- Sponsoring Org:
- National Science Foundation
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