| Title: | Evaluation of deep learning models for image-based classification of timber logs by market value |
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| Authors: | ID Triplat, Matevž (Author)
ID Lukančič, Žiga (Author)
ID Kavčič, Vasja (Author) |
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| Files: |  URL - Source URL, visit https://www.mdpi.com/1999-4907/17/5/518
 PDF - Presentation file, download (4,84 MB)
MD5: 1DD0C7041CC424F06263C22D47AFF197
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  SciVie - Slovenian Forestry Institute
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| Abstract: | The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable practices. This study addresses this by classifying images of timber logs by tree species and market value using the Orange data mining software, which leverages pre-trained convolutional neural networks (Inception v3 and SqueezeNet) to generate embeddings from a dataset of 5549 images collected at a real timber auction in Slovenia, followed by logistic regression image classification. Results show high accuracy for tree species classification (up to 92.6%), but substantially lower accuracy for market value classification (40%–55%), reflecting the greater complexity of value determination from visual features. These findings underscore the promise of deep learning for species identification while indicating the need for further methodological advancements to enhance value classification reliability, which offers the practical impact for operational forestry and bioeconomy value chains. |
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| Keywords: | image classification, timber quality, high value assortments, auctions, wood products, convolutional neural networks, CNNs, non-destructive evaluation, machine learning in forestry, tree species image recognition, forest wood assortment value |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-15 |
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| Numbering: | Vol. 17, iss. 5, [article no.] 518 |
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| PID: | 20.500.12556/DiRROS-30071  |
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| UDC: | 630*7 |
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| ISSN on article: | 1999-4907 |
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| DOI: | 10.3390/f17050518 |
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| COBISS.SI-ID: | 281502723 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 12. 6. 2026;
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| Publication date in DiRROS: | 12.06.2026 |
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| Views: | 35 |
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| Downloads: | 18 |
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