[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120127-en":3,"doc-seo-120127-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120127,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based wood anatomy identification - towards anatomical feature recognition","Machine learning and computer vision are applied to wood identification by mapping digital images of wood sections or surfaces to tree species, but current image-to-species approaches are constrained by limited, high-quality reference databases and the inability to identify species absent from those references. Feature extraction also acts as a black box, creating a mismatch between machine-learning features and recognizable wood anatomical traits. The work surveys existing feature extraction, classification, and deep-learning methods, highlights pitfalls and opportunities, and proposes an image-to-features-to-species framework using microscopic images and text-based anatomical descriptions to support trait-level, (semi-)automated expert-style identification.","Machine learning-based wood anatomy identification: towards anatomical feature recognition  \nHe, X.; Pelt, D.M.; Gao, J.R.; Gravendeel, B.; Zhu, P.Q.; Chen, S.Y.; ... ; Lens, F.P.  \nCitation  \nHe, X., Pelt, D. M., Gao, J. R., Gravendeel, B., Zhu, P. Q., Chen, S. Y.,   Lens, F. P. (2024) . Machine learning-based wood anatomy identification: towards anatomical feature recognition. Iawa Journal, 1-19. doi:10.1163/22941932-bja10157  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/4094350](https://hdl.handle.net/1887/4094350)  \nNote: To cite this publication please use the final published version (if applicable) .  \nIAWA Journal 0 (0), 2024: 1-19   \nCommentary  \nMachine learning-based wood anatomy identification: towards anatomical feature recognition  \nXin He 1 , Daniël M. Pelt 2 , JingRan Gao 1 , Barbara Gravendeel 3 , PeiQi Zhu 1 , SongYang Chen 1 , Jian Qiu 1,⁎ and Frederic Lens 3,4,⁎  \n1 Southwest Forestry University, Kunming, P.R. China  \n2 Leiden Institute ofAdvanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands  \n3 Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA Leiden, The Netherlands  \n4 Leiden University, Institute of Biology Leiden, Plant Sciences, Sylvius weg 72, 2333 BE Leiden, The Netherlands  \n*Corresponding authors; [emails: qiujian@swfu.edu.cn](emails: qiujian@swfu.edu.cn);[frederic.lens@naturalis.nl](frederic.lens@naturalis.nl)  \nORCID iDs: He: 0000-0001-8800-7036; Pelt: 0000-0002-8253-0851; Gao: 0009-0006-3361-1002;  \nGravendeel: 0000-0002-6508-0895; Zhu: 0009-0009-2407-8818; Chen: 0009-0006-1553-068X;  \nQiu: 0009-0007-9487-169X; Lens: 0000-0002-5001-0149  \nAccepted for publication: 5 April 2024; published online: 27 April 2024  \nSummary – Computer vision-based wood identification has been successfully applied to recognize tree species using digital images of wood sections or surfaces. However, this image-to-species approach can only recognize a limited number of species due to two main reasons: 1) the lack of a good reference database requiring high-quality standardized images from multiple individuals of hundreds or even thousands of traded timber species, and 2) species not included in the reference database cannot be identified without expert knowledge. Another bottleneck is that the feature extraction process used by these species recognition approaches is a black box, thereby creating a discrepancy between machine learning features and wood anatomical features. This discrepancy prevents wood anatomists from understanding how these machine-learning algorithms work. Here, we survey currently existing methods used in feature extraction, classification, and deep learning methods applied in wood identification along with their pitfalls and opportunities. As an example of how the field could move forward, we launch the idea of building an image-to-features-to-species identification approach based on microscopic wood images as well as text files comprising wood anatomical descriptions. If we can manage machine learning-based algorithms to recognize the main wood anatomical traits that experts use to identify species in a (semi-)automated way, this would boost wood identification in two ways: (1) extensive reference databases for each species would become less crucial as the databases are ordered at the trait level,(2) timber identification would become more feasible for species that have not yet been included in the reference database as long as wood anatomical descriptions are available.  \nKeywords – feature incompatibility, illegal logging, species recognition.  \nIntroduction  \nWood identification plays a vital role in forest law enforcement, conservation, timber industry, and scientific research. Accurate identification helps the fight against illegal logging, as the combination of illegal and legal logging  \n© International Association of Wood Anatomists, 2024 DOI 10.1163/22941932-bja10","cbCaivBPizdszni8","https://ap.wps.com/l/cbCaivBPizdszni8","pdf",4156040,1,20,"English","en",105,"# Summary\n## Core problem and limitations\n## Feature extraction discrepancy\n## Survey and proposed trait-based framework\n## Application and impact","[{\"question\":\"Why do current image-to-species wood identification methods struggle in practice?\",\"answer\":\"They rely on reference databases that require high-quality standardized images and cannot identify species missing from the database without expert knowledge.\"},{\"question\":\"What is the main issue caused by black-box feature extraction in wood recognition?\",\"answer\":\"It creates a discrepancy between machine-learning features and actual wood anatomical features, preventing wood anatomists from understanding how the algorithms work.\"},{\"question\":\"What approach is proposed to improve wood identification and reduce dependence on large databases?\",\"answer\":\"An image-to-features-to-species pipeline based on microscopic wood images combined with text files describing wood anatomical traits, enabling (semi-)automated recognition of key anatomical characteristics.\"}]","Machine learning-based wood anatomy identification - towards anatomical feature recognition | PDF",1785728339,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-wood-anatomy-identification-towards-anatomical-feature-recognition","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-wood-anatomy-identification-towards-anatomical-feature-recognition/120127/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do current image-to-species wood identification methods struggle in practice?","Question",{"text":75,"@type":76},"They rely on reference databases that require high-quality standardized images and cannot identify species missing from the database without expert knowledge.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main issue caused by black-box feature extraction in wood recognition?",{"text":80,"@type":76},"It creates a discrepancy between machine-learning features and actual wood anatomical features, preventing wood anatomists from understanding how the algorithms work.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach is proposed to improve wood identification and reduce dependence on large databases?",{"text":84,"@type":76},"An image-to-features-to-species pipeline based on microscopic wood images combined with text files describing wood anatomical traits, enabling (semi-)automated recognition of key anatomical characteristics.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]