Wood Quality, Smart Timber Harvesting, and Forestry Machinery

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Operations and Engineering".

Deadline for manuscript submissions: 25 June 2026 | Viewed by 614

Special Issue Editors


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Guest Editor
Department of AGRARIA, Mediterranean University of Reggio Calabria, Feo di Vito snc, 89122 Reggio Calabria, Italy
Interests: forest operations; wood supply chain; wood harvesting; time–motion study; productivity study; forest worksites management; wood quality estimation; non-destructive techniques (NDT); wood technology; bioenergy; recovery of waste wood material
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department AGRARIA, University Mediterranea of Reggio Calabria, Feo di Vito snc, 89122 Reggio Calabria, Italy
Interests: forest operations; timber harvesting systems; bioenergy; logistics; ergonomics and safety; time and motion studies; productivity study; optimization; artificial intelligence; recovery of waste wood material
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on “Wood Quality, Smart Timber Harvesting, and Forestry Machinery” invites contributions that improve operational efficiency, safety, and sustainability along the entire timber value chain. We welcome studies on forestry operations and timber harvesting that integrate time-to-movement analysis and productivity assessment to optimize cycles, reduce costs, and minimize environmental impact. Articles on forestry site management, encompassing planning, logistics, and decision support, including applications of sensorized machinery, GNSS/IMU tracking, digital twins, and AI-assisted planning, are encouraged. Contributions on wood technology that present methods for estimating wood quality, with a focus on non-destructive techniques (NDT), such as stress wave velocity, resistography, acoustic tomography, and dynamic MOE, and that link raw material properties to product performance, are welcome, as are studies targeting resilient wood supply chains under climate change and green economy. Finally, we encourage research on bioenergy and wood waste recovery, including cascading use, circular design, and low-carbon pathways. Both field experiments and modelling articles are welcome, as are case studies, protocols, and reviews that translate innovation into practice for sustainable and smart forestry.

Dr. Maria Francesca Cataldo
Dr. Salvatore F. Papandrea
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wood supply chain
  • precision forestry
  • decision support systems (DSS)
  • life cycle assessment (LCA)
  • timber traceability
  • waste wood recovery
  • worksite management
  • human–machine interaction (HMI)
  • non-destructive testing (NDT)
  • wood transportation

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Published Papers (1 paper)

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Research

20 pages, 5234 KB  
Article
Performance of Neural Networks in Automated Detection of Wood Features in CT Images
by Tomáš Gergeľ, Ondrej Vacek, Miloš Gejdoš, Diana Zraková, Peter Balogh and Emil Ješko
Forests 2026, 17(4), 425; https://doi.org/10.3390/f17040425 - 27 Mar 2026
Viewed by 399
Abstract
Computed tomography (CT) enables non-destructive insight into internal log structure, yet fully automated interpretation of CT images remains limited by inconsistent annotations, boundary ambiguity, and insufficient spatial context in 2D slice-based analysis. These challenges restrict the industrial deployment of deep learning for wood [...] Read more.
Computed tomography (CT) enables non-destructive insight into internal log structure, yet fully automated interpretation of CT images remains limited by inconsistent annotations, boundary ambiguity, and insufficient spatial context in 2D slice-based analysis. These challenges restrict the industrial deployment of deep learning for wood quality assessment. This study applies artificial intelligence (AI) and deep learning to the automated analysis of computed tomography (CT) scans of wood logs for detecting internal qualitative features and segmenting bark. Using convolutional neural networks (CNNs), trained models accurately distinguish healthy and damaged regions and segment bark, including discontinuous parts. We introduce a novel pseudo-spatial representation by merging consecutive slices into red–green–blue (RGB) format, which improves prediction accuracy and model robustness across logs. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) highlights regions contributing most to defect detection, particularly knots. Comprehensive evaluation using Sørensen–Dice similarity coefficients and confusion matrices confirms the effectiveness of the proposed approach under industrial conditions. These findings demonstrate that AI-driven CT image analysis can address key limitations of current log-grading workflows and enable more reliable, objective, and scalable quality assessment for timber-dependent economies. Full article
(This article belongs to the Special Issue Wood Quality, Smart Timber Harvesting, and Forestry Machinery)
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