Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks
Abstract Accurate roundwood sorting is critical for improving resource efficiency in the timber industry. However, in many small sawmills, sorting is performed manually through visual inspection, often resulting in inconsistencies and misclassifications. Sorting wood based on macroscopic images usin...
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SpringerOpen
2025-05-01
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| Series: | Journal of Wood Science |
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| Online Access: | https://doi.org/10.1186/s10086-025-02197-x |
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| author | Julia Achatz Mark Schubert |
| author_facet | Julia Achatz Mark Schubert |
| author_sort | Julia Achatz |
| collection | DOAJ |
| description | Abstract Accurate roundwood sorting is critical for improving resource efficiency in the timber industry. However, in many small sawmills, sorting is performed manually through visual inspection, often resulting in inconsistencies and misclassifications. Sorting wood based on macroscopic images using convolutional neural networks (CNN) is a cost-effective and efficient approach. However, current models focus primarily on cross-sectional images, limiting their ability to detect important features like knots and beetle infestations. We present an industrial dataset of 5,200 samples, each including both a trunk and a cross-sectional image. Using this dataset, we developed two models: a stem quality classification model and a beetle-indicator detection model. The first model uses only trunk images to extract stem information, supporting existing cross-sectional models. The beetle-indicator detection model utilizes both trunk and cross-sectional images, introducing a new detection capability to computer vision-based roundwood sorting. To ensure transparency and interpretability, we applied Explainable Artificial Intelligence (XAI) techniques to highlight key regions within the images that influenced the models’ predictions. The stem quality model achieves 80% accuracy in trunk feature analysis, and the beetle-indicator model reaches 89% accuracy. Together, these models improve automation based on convolutional neural networks by enabling detailed trunk analysis and early beetle infestation detection. |
| format | Article |
| id | doaj-art-ef41d36f93f04ee188a722ee74b68b94 |
| institution | OA Journals |
| issn | 1611-4663 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Wood Science |
| spelling | doaj-art-ef41d36f93f04ee188a722ee74b68b942025-08-20T01:52:24ZengSpringerOpenJournal of Wood Science1611-46632025-05-0171111310.1186/s10086-025-02197-xOptical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networksJulia Achatz0Mark Schubert1Cellulose and Wood Materials, Group WoodTec, Empa Material Science and TechnologyCellulose and Wood Materials, Group WoodTec, Empa Material Science and TechnologyAbstract Accurate roundwood sorting is critical for improving resource efficiency in the timber industry. However, in many small sawmills, sorting is performed manually through visual inspection, often resulting in inconsistencies and misclassifications. Sorting wood based on macroscopic images using convolutional neural networks (CNN) is a cost-effective and efficient approach. However, current models focus primarily on cross-sectional images, limiting their ability to detect important features like knots and beetle infestations. We present an industrial dataset of 5,200 samples, each including both a trunk and a cross-sectional image. Using this dataset, we developed two models: a stem quality classification model and a beetle-indicator detection model. The first model uses only trunk images to extract stem information, supporting existing cross-sectional models. The beetle-indicator detection model utilizes both trunk and cross-sectional images, introducing a new detection capability to computer vision-based roundwood sorting. To ensure transparency and interpretability, we applied Explainable Artificial Intelligence (XAI) techniques to highlight key regions within the images that influenced the models’ predictions. The stem quality model achieves 80% accuracy in trunk feature analysis, and the beetle-indicator model reaches 89% accuracy. Together, these models improve automation based on convolutional neural networks by enabling detailed trunk analysis and early beetle infestation detection.https://doi.org/10.1186/s10086-025-02197-xRoundwood sortingTrunk datasetArtificial intelligenceConvolutional neural networkBeetle wood indicator detection |
| spellingShingle | Julia Achatz Mark Schubert Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks Journal of Wood Science Roundwood sorting Trunk dataset Artificial intelligence Convolutional neural network Beetle wood indicator detection |
| title | Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks |
| title_full | Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks |
| title_fullStr | Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks |
| title_full_unstemmed | Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks |
| title_short | Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks |
| title_sort | optical detection of beetle related indicators and stem quality in roundwood using convolutional neural networks |
| topic | Roundwood sorting Trunk dataset Artificial intelligence Convolutional neural network Beetle wood indicator detection |
| url | https://doi.org/10.1186/s10086-025-02197-x |
| work_keys_str_mv | AT juliaachatz opticaldetectionofbeetlerelatedindicatorsandstemqualityinroundwoodusingconvolutionalneuralnetworks AT markschubert opticaldetectionofbeetlerelatedindicatorsandstemqualityinroundwoodusingconvolutionalneuralnetworks |