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Article

The Performance of a Novel Automated Algorithm in Estimating Truckload Volume Based on LiDAR Data

by
Mihai Daniel Niţă
1,2,3,
Cătălin Cucu-Dumitrescu
2,4,
Bogdan Candrea
2,3,
Bogdan Grama
2,
Iulian Iuga
2 and
Stelian Alexandru Borz
1,*
1
Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Braşov, Şirul Beethoven 1, 500123 Braşov, Romania
2
Forest Core, B-dul Basarabia 256G, 030352 Bucharest, Romania
3
Forest Design, Nicovalei 33, 500473 Brasov, Romania
4
Laboratory 1030—Space Applications and Technologies, Institute of Space Science—INFLPR Subsidiary, Atomiștilor 409, 077125 Măgurele, Romania
*
Author to whom correspondence should be addressed.
Forests 2025, 16(8), 1281; https://doi.org/10.3390/f16081281
Submission received: 11 April 2025 / Revised: 17 July 2025 / Accepted: 31 July 2025 / Published: 5 August 2025
(This article belongs to the Section Forest Operations and Engineering)

Abstract

Significant improvements in the forest-based industrial sector are expected due to increased digitalization; however, examples of practical implementations remain limited. This study explores the use of an automated algorithm to estimate truckload volumes based on 3D point cloud data acquired using two different LiDAR scanning platforms. This research compares the performance of a professional mobile laser scanning (MLS GeoSLAM) platform and a smartphone-based iPhone LiDAR system. A total of 48 truckloads were measured using a combination of manual, factory-based, and digital approaches. Accuracy was evaluated using standard error metrics, including the mean absolute error (MAE) and root mean square error (RMSE), with manual or factory references used as benchmarks. The results showed a strong correlation and no significant differences between the algorithmic and manual measurements when using the MLS platform (MAE = 2.06 m3; RMSE = 2.46 m3). For the iPhone platform, the results showed higher deviations and significant overestimation compared to the factory reference (MAE = 3.29 m3; RMSE = 3.60 m3). Despite these differences, the iPhone platform offers real-time acquisition and low-cost deployment. These findings highlight the trade-offs between precision and operational efficiency and support the adoption of automated measurement tools in timber supply chains.
Keywords: sustainability; digitization; wood supply; transportation; sourcing; point cloud processing; automation sustainability; digitization; wood supply; transportation; sourcing; point cloud processing; automation

Share and Cite

MDPI and ACS Style

Niţă, M.D.; Cucu-Dumitrescu, C.; Candrea, B.; Grama, B.; Iuga, I.; Borz, S.A. The Performance of a Novel Automated Algorithm in Estimating Truckload Volume Based on LiDAR Data. Forests 2025, 16, 1281. https://doi.org/10.3390/f16081281

AMA Style

Niţă MD, Cucu-Dumitrescu C, Candrea B, Grama B, Iuga I, Borz SA. The Performance of a Novel Automated Algorithm in Estimating Truckload Volume Based on LiDAR Data. Forests. 2025; 16(8):1281. https://doi.org/10.3390/f16081281

Chicago/Turabian Style

Niţă, Mihai Daniel, Cătălin Cucu-Dumitrescu, Bogdan Candrea, Bogdan Grama, Iulian Iuga, and Stelian Alexandru Borz. 2025. "The Performance of a Novel Automated Algorithm in Estimating Truckload Volume Based on LiDAR Data" Forests 16, no. 8: 1281. https://doi.org/10.3390/f16081281

APA Style

Niţă, M. D., Cucu-Dumitrescu, C., Candrea, B., Grama, B., Iuga, I., & Borz, S. A. (2025). The Performance of a Novel Automated Algorithm in Estimating Truckload Volume Based on LiDAR Data. Forests, 16(8), 1281. https://doi.org/10.3390/f16081281

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