{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:03:29Z","timestamp":1783699409740,"version":"3.55.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006393","name":"Universidad de Granada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006393","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Tools for automatic wood species identification are needed worldwide in order to support sustainable timber trade. This work explores the application of computer vision techniques to classify high-resolution macroscopic images of timber. The main challenge of this problem is that fine-grained patterns in timber are crucial in order to accurately identify wood species, and these patterns are not learned by convolutional neural networks (CNNs) trained on low resolution images. This work introduces the Timber Deep Learning Identification with Patch-based Inference Voting methodology, abbreviated TDLI-PIV methodology. This methodology exploits the concept of patching and the availability of high-resolution macroscopic images of timber in order to overcome the inherent challenges that CNNs face in timber identification. The TDLI-PIV methodology is able to capture fine-grained patterns in timber and, moreover, boosts robustness and prediction accuracy via a collaborative voting inference process. In this work we also introduce a new data set of marcroscopic images of timber, called GOIMAI-Phase-I, which has been obtained using optical magnification, and is openly published online in zenodo. Our experiments have assessed the performance of the TDLI-PIV methodology, including a comparison with other methodologies available in the literature, an exploration of data augmentation methods and the effect that the dataset size has on the accuracy of TDLI-PIV.<\/jats:p>","DOI":"10.1007\/s10489-025-06731-8","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T04:35:48Z","timestamp":1772685348000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning methodology for the identification of wood species using high-resolution macroscopic images and patch-voting"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2604-2078","authenticated-orcid":false,"given":"David","family":"Herrera-Poyatos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9s","family":"Herrera-Poyatos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosana","family":"Montes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paloma","family":"de Palacios","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis G.","family":"Esteban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Garc\u00eda Iruela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Garc\u00eda Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"6731_CR1","doi-asserted-by":"crossref","unstructured":"FAO (2020) Evaluaci\u00f3n de los recursos forestales mundiales 2020 \u2013 Principales resultados. Available at https:\/\/doi.org\/10.4060\/ca8753es","DOI":"10.4060\/ca8753es"},{"key":"6731_CR2","unstructured":"Barber CV, Canby K (2018) Assessing the timber legality strategy in tackling deforestation. World Resources Institute, Washington. Available at https:\/\/www.wri.org\/research\/ending-tropical-deforestation-assessing-timber-legality-strategy-tackling-deforestation"},{"key":"6731_CR3","unstructured":"Lawson S (2014) Consumer Goods and Deforestation: An Analysis of the Extent and Nature of Illegality in Forest Conversion for Agriculture and Timber Plantations. Available at https:\/\/www.forest-trends.org\/publications\/consumer-goods-and-deforestation\/"},{"key":"6731_CR4","unstructured":"CITES (2018) CONVENTION ON INTERNATIONAL TRADE IN ENDANGERED SPECIES OF WILD FAUNA AND FLORA. Available at https:\/\/cites.org\/eng\/disc\/what.php"},{"issue":"1","key":"6731_CR5","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1163\/22941932-90000206","volume":"30","author":"LG Esteban","year":"2009","unstructured":"Esteban LG, Fern\u00e1ndez FG, Palacios PdP, Romero RM, Cano NN (2009) Artificial neural networks in wood identification: the case of two juniperus species from the canary islands. IAWA J 30(1):87\u201394","journal-title":"IAWA J"},{"key":"6731_CR6","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1007\/s00226-017-0932-7","volume":"51","author":"LG Esteban","year":"2017","unstructured":"Esteban LG, Palacios P, Conde M, Fern\u00e1ndez FG, Garc\u00eda-Iruela A, Gonz\u00e1lez-Alonso M (2017) Application of artificial neural networks as a predictive method to differentiate the wood of pinus sylvestris l. and pinus nigra arn subsp. salzmannii (dunal) franco. Wood Sci Technol 51:1249\u20131258","journal-title":"Wood Sci Technol"},{"issue":"1","key":"6731_CR7","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/f11010036","volume":"11","author":"T He","year":"2019","unstructured":"He T, Marco J, Soares R, Yin Y, Wiedenhoeft AC (2019) Machine learning models with quantitative wood anatomy data can discriminate between swietenia macrophylla and swietenia mahagoni. Forests 11(1):36","journal-title":"Forests"},{"issue":"1","key":"6731_CR8","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s13007-021-00746-1","volume":"17","author":"S-W Hwang","year":"2021","unstructured":"Hwang S-W, Sugiyama J (2021) Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review. Plant Methods 17(1):47","journal-title":"Plant Methods"},{"issue":"3","key":"6731_CR9","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s00226-021-01282-w","volume":"55","author":"AR Geus","year":"2021","unstructured":"Geus AR, Backes AR, Gontijo AB, Albuquerque GHQ, Souza JR (2021) Amazon wood species classification: a comparison between deep learning and pre-designed features. Wood Sci Technol 55(3):857\u2013872","journal-title":"Wood Sci Technol"},{"key":"6731_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105941","volume":"181","author":"A Fabija\u0144ska","year":"2021","unstructured":"Fabija\u0144ska A, Danek M, Barniak J (2021) Wood species automatic identification from wood core images with a residual convolutional neural network. Comput Electron Agric 181:105941","journal-title":"Comput Electron Agric"},{"key":"6731_CR11","doi-asserted-by":"crossref","unstructured":"Figueroa-Mata G, Mata-Montero E, Valverde-Ot\u00e1rola JC, Arias-Aguilar D, Zamora-Villalobos N (2022) Using deep learning to identify costa rican native tree species from wood cut images. Front Plant Sci 13","DOI":"10.3389\/fpls.2022.789227"},{"key":"6731_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2022.101633","volume":"69","author":"\u0130 K\u0131rba\u015f","year":"2022","unstructured":"K\u0131rba\u015f \u0130, \u00c7ifci A (2022) An effective and fast solution for classification of wood species: A deep transfer learning approach. Ecological Inf 69:101633","journal-title":"Ecological Inf"},{"key":"6731_CR13","doi-asserted-by":"crossref","unstructured":"Chun TH, Hashim UR, Ahmad S, Salahuddin L, Choon NH, Kanchymalay K (2022) Efficacy of the image augmentation method using cnn transfer learning in identification of timber defect. Int J Adv Comput Sci Appl 13(5)","DOI":"10.14569\/IJACSA.2022.0130514"},{"key":"6731_CR14","doi-asserted-by":"crossref","unstructured":"Kim J-H, Purusatama BD, Savero AM, Prasetia D, Yang G-U, Han S-Y, Lee S-H, Kim N-H (2023) Performance influencing factors of convolutional neural network models for classifying certain softwood species. Forests 14(6)","DOI":"10.3390\/f14061249"},{"key":"6731_CR15","doi-asserted-by":"crossref","unstructured":"Nguyen-Trong K (2023) Evaluation of wood species identification using cnn-based networks at different magnification levels. Int J Adv Comput Sci Appl 14(4)","DOI":"10.14569\/IJACSA.2023.0140487"},{"key":"6731_CR16","doi-asserted-by":"publisher","unstructured":"Urbano CFO, Vargas-Ca\u00f1as R, Mari\u00f1o NMD (2023) IMACA\u2013Automated wood identification system of Colombian timber species using convolutional neural networks. Available at Research Square https:\/\/doi.org\/10.21203\/rs.3.rs-3640320\/v1","DOI":"10.21203\/rs.3.rs-3640320\/v1"},{"key":"6731_CR17","doi-asserted-by":"crossref","unstructured":"Zheng Z, Ge Z, Yang X, Liu X, Qin L, Wang X, Zhou Y (2024) A-repvgg: Research on classification algorithms based on deep learning and wood ct images","DOI":"10.21203\/rs.3.rs-4021077\/v1"},{"key":"6731_CR18","doi-asserted-by":"crossref","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2017) High-resolution image classification with convolutional networks. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS), pp 5157\u20135160","DOI":"10.1109\/IGARSS.2017.8128163"},{"key":"6731_CR19","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s00371-017-1424-3","volume":"35","author":"G Cao","year":"2019","unstructured":"Cao G, Li J, Chen X, He Z (2019) Patch-based self-adaptive matting for high-resolution image and video. Visual Comput 35:133\u2013147","journal-title":"Visual Comput"},{"key":"6731_CR20","doi-asserted-by":"publisher","first-page":"24273","DOI":"10.1109\/ACCESS.2021.3056516","volume":"9","author":"I Hirra","year":"2021","unstructured":"Hirra I, Ahmad M, Hussain A, Ashraf MU, Saeed IA, Qadri SF, Alghamdi AM, Alfakeeh AS (2021) Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access 9:24273\u201324287","journal-title":"IEEE Access"},{"key":"6731_CR21","unstructured":"Montes R, Herrera-Poyatos D, Herrera-Poyatos A, Herrera F, Palacios P, De\u00a0Marco A, Garc\u00eda-Iruela A, Garcia-Fern\u00e1ndez F, G.\u00a0Esteban L (2024) GOIMAI DataSet-Phase-I of high-resolution macroscopic images for the identification of wood species. Available for download at https:\/\/zenodo.org\/doi\/10.5281\/zenodo.11092208"},{"key":"6731_CR22","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.compag.2017.12.011","volume":"144","author":"P Barmpoutis","year":"2018","unstructured":"Barmpoutis P, Dimitropoulos K, Barboutis I, Grammalidis N, Lefakis P (2018) Wood species recognition through multidimensional texture analysis. Comput Electron Agric 144:241\u2013248","journal-title":"Comput Electron Agric"},{"issue":"4","key":"6731_CR23","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1007\/s00226-020-01196-z","volume":"54","author":"DV Souza","year":"2020","unstructured":"Souza DV, Santos JX, Vieira HC, Naide TL, Nisgoski S, Oliveira LES (2020) An automatic recognition system of brazilian flora species based on textural features of macroscopic images of wood. Wood Sci Technol 54(4):1065\u20131090","journal-title":"Wood Sci Technol"},{"key":"6731_CR24","doi-asserted-by":"publisher","DOI":"10.2737\/FPL-RN-367","volume-title":"The Xyloscope: A Field-deployable Macroscopic Digital Imaging Device for Wood","author":"JC Hermanson","year":"2019","unstructured":"Hermanson JC, Dostal D, Destree JC, Wiedenhoeft AC et al (2019) The Xyloscope: A Field-deployable Macroscopic Digital Imaging Device for Wood. United States Department of Agriculture, Forest Service, Forest Products Laboratory"},{"key":"6731_CR25","unstructured":"Ravindran P, Ebanyenle E, Ebeheakey AA, Abban KB, Lambog O, Soares R, Costa A, Wiedenhoeft AC (2019) Image based identification of ghanaian timbers using the xylotron: Opportunities, risks and challenges. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. arXiv:1912.00296"},{"key":"6731_CR26","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.3389\/fpls.2020.01015","volume":"11","author":"P Ravindran","year":"2020","unstructured":"Ravindran P, Thompson BJ, Soares RK, Wiedenhoeft AC (2020) The xylotron: flexible, open-source, image-based macroscopic field identification of wood products. Front Plant Sci 11:1015","journal-title":"Front Plant Sci"},{"key":"6731_CR27","doi-asserted-by":"crossref","unstructured":"Tang XJ, Tay YH, Siam NA, Lim SC (2018) Mywood-id: Automated macroscopic wood identification system using smartphone and macro-lens. In: Proceedings of the 2018 international conference on computational intelligence and intelligent systems. CIIS 2018. Association for Computing Machinery, New York, NY, USA,pp 37\u201343","DOI":"10.1145\/3293475.3293493"},{"key":"6731_CR28","doi-asserted-by":"crossref","unstructured":"Verly\u00a0Lopes DJ, Burgreen GW, Entsminger ED (2020) North american hardwoods identification using machine-learning. Forests 11(3)","DOI":"10.3390\/f11030298"},{"key":"6731_CR29","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.eswa.2018.10.010","volume":"118","author":"A G\u00f3mez-R\u00edos","year":"2019","unstructured":"G\u00f3mez-R\u00edos A, Tabik S, Luengo J, Shihavuddin A, Krawczyk B, Herrera F (2019) Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. Expert Syst Appl 118:315\u2013328","journal-title":"Expert Syst Appl"},{"key":"6731_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107238","volume":"104","author":"S Pathan","year":"2021","unstructured":"Pathan S, Siddalingaswamy PC, Ali T (2021) Automated detection of covid-19 from chest x-ray scans using an optimized cnn architecture. Appl Soft Comput 104:107238","journal-title":"Appl Soft Comput"},{"key":"6731_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109102","volume":"125","author":"A Karsaz","year":"2022","unstructured":"Karsaz A (2022) A modified convolutional neural network architecture for diabetic retinopathy screening using svdd. Appl Soft Comput 125:109102. https:\/\/doi.org\/10.1016\/j.asoc.2022.109102","journal-title":"Appl Soft Comput"},{"key":"6731_CR32","doi-asserted-by":"crossref","unstructured":"Palacios P, Esteban G, L, Gasson P, Garc\u00eda-Fern\u00e1ndez F, Marco A, Garc\u00eda-Iruela A, Garc\u00eda-Esteban L, Gonz\u00e1lez-de-Vega D (2020) Using lenses attached to a smartphone as a macroscopic early warning tool in the illegal timber trade, in particular for cites-listed species. Forests 11(11):1147","DOI":"10.3390\/f11111147"},{"key":"6731_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.122166","volume":"713","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Zhu H, Wang H, Jamil R, Zhou F, Xiao C, Fujita H, Aljuaid H (2025) Image deblurring method based on gan with a channel attention mechanism. Inf Sci 713:122166. https:\/\/doi.org\/10.1016\/j.ins.2025.122166","journal-title":"Inf Sci"},{"key":"6731_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.122303","volume":"717","author":"X Jiang","year":"2025","unstructured":"Jiang X, Wang B, Wan X, Chen S, Fujita H, Aljuaid H (2025) Project-and-fuse: Improving rgb-d semantic segmentation via graph convolution networks. Information Sciences 717:122303. https:\/\/doi.org\/10.1016\/j.ins.2025.122303","journal-title":"Information Sciences"},{"key":"6731_CR35","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1007\/s00138-014-0592-7","volume":"25","author":"PLP Filho","year":"2014","unstructured":"Filho PLP, Oliveira LS, Nisgoski S, Britto AS (2014) Forest species recognition using macroscopic images. Mach Vision Appl 25:1019\u20131031","journal-title":"Mach Vision Appl"},{"key":"6731_CR36","doi-asserted-by":"crossref","unstructured":"Cano\u00a0Saenz DA, Ordo\u00f1ez\u00a0Urbano CF, Gaitan\u00a0Mesa HR, Vargas-Ca\u00f1as R (2022) Tropical wood species recognition: A dataset of macroscopic images. Data 7(8)","DOI":"10.3390\/data7080111"},{"key":"6731_CR37","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"6731_CR38","unstructured":"Tan M, Le Q (2021) Efficientnetv2: Smaller models and faster training. In: International conference on machine learning, PMLR, pp 10096\u201310106"},{"issue":"2","key":"6731_CR39","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1145\/3517337","volume":"29","author":"L Aroyo","year":"2022","unstructured":"Aroyo L, Lease M, Paritosh P, Schaekermann M (2022) Data excellence for ai: why should you care? Interactions 29(2):66\u201369","journal-title":"Interactions"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06731-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06731-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06731-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:23:08Z","timestamp":1781637788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06731-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,5]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["6731"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06731-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,5]]},"assertion":[{"value":"13 June 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2026","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"128"}}