Identifying non-thrive trees and predicting wood density from resistograph using temporal convolution network

Abstract 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.