Accuracy of a Smartphone-based freeware solution and two shape reconstruction algorithms in log volume measurements

Abstract

Enhancing accuracy, mobility, safety, ergonomics, data transfer and traceability functions in log measurement applications are prerequisites of modern business in the wood supply chain. Various methods and methodologies have been tested so far to overcome the current limitations in the attempt to move the wood supply chain to Forestry 4.0 concepts, many of which still share the same problem, namely the high costs incurred by data acquisition. A freeware application was used in this study to collect point cloud data on 612 logs of various sizes and two external shape reconstruction algorithms, namely Random Sampling and Consensus (RANSAC) and Poisson interpolation, were then used to produce volume estimates under a mobile scanning approach (MS). These were compared against the estimates produced by a highly-detailed manual measurement (MM). At the sample level, which included logs reaching volumes of more than 3.5 m3, the differential bias of the methods ranged between 0.037 and 0.075 m3, while the errors metrics such as the mean absolute error (MAE), mean squared error (MSE) and root mean square error (RMSE) were between 0.047 and 0.081, 0.006 to 0.023 and 0.076 to 0.151 m3. The results indicate that, although there was a proportional bias in MS estimates, the approach would be useful for logs in size of 0.25 to 0.40 m3, while these log sizes largely resemble the characteristics of the wood supply in many areas of the world. Further studies should focus on the capability and accuracy of the tested application to instantly produce accurate estimates, as well as on the integration of deep learning to produce better estimates.