{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tree-species-identification-from-bark-images","title":"Tree Species Identification from Bark Images Using Convolutional Neural Networks","arxiv_id":"1803.00949","date":"2018-03-02","proceeding":null,"authors":["Mathieu Carpentier","Philippe Giguère","Jonathan Gaudreault"],"abstract":"Tree species identification using bark images is a challenging problem that\ncould prove useful for many forestry related tasks. However, while the recent\nprogress in deep learning showed impressive results on standard vision\nproblems, a lack of datasets prevented its use on tree bark species\nclassification. In this work, we present, and make publicly available, a novel\ndataset called BarkNet 1.0 containing more than 23,000 high-resolution bark\nimages from 23 different tree species over a wide range of tree diameters. With\nit, we demonstrate the feasibility of species recognition through bark images,\nusing deep learning. More specifically, we obtain an accuracy of 93.88% on\nsingle crop, and an accuracy of 97.81% using a majority voting approach on all\nof the images of a tree. We also empirically demonstrate that, for a fixed\nnumber of images, it is better to maximize the number of tree individuals in\nthe training database, thus directing future data collection efforts.","url_abs":"http://arxiv.org/abs/1803.00949v2","url_pdf":"http://arxiv.org/pdf/1803.00949v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tree-species-identification-from-bark-images","repo_url":"https://github.com/ulaval-damas/tree-bark-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"tree-species-identification-from-bark-images","repo_url":"https://github.com/Sehaba95/Bark-Idenfication-using-BarkNet-1.0","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[{"slug":"barknet-1-0","name":"BarkNet 1.0","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}