Classification of CITES-Listed and Other Neotropical Meliaceae Wood Images Using Convolutional Neural Networks
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Ravindran et al. Plant Methods (2018) 14:25 https://doi.org/10.1186/s13007-018-0292-9 Plant Methods METHODOLOGY Open Access Classifcation of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks Prabu Ravindran1,2, Adriana Costa1,2, Richard Soares1,2 and Alex C. Wiedenhoeft1,2,3,4* Abstract Background: The current state-of-the-art for feld wood identifcation to combat illegal logging relies on experi- enced practitioners using hand lenses, specialized identifcation keys, atlases of woods, and feld manuals. Accumula- tion of this expertise is time-consuming and access to training is relatively rare compared to the international demand for feld wood identifcation. A reliable, consistent and cost efective feld screening method is necessary for efective global scale enforcement of international treaties such as the Convention on the International Trade in Endagered Species (CITES) or national laws (e.g. the US Lacey Act) governing timber trade and imports. Results: We present highly efective computer vision classifcation models, based on deep convolutional neural networks, trained via transfer learning, to identify the woods of 10 neotropical species in the family Meliaceae, includ- ing CITES-listed Swietenia macrophylla, Swietenia mahagoni, Cedrela fssilis, and Cedrela odorata. We build and evaluate models to classify the 10 woods at the species and genus levels, with image-level model accuracy ranging from 87.4 to 97.5%, with the strongest performance by the genus-level model. Misclassifed images are attributed to classes consistent with traditional wood anatomical results, and our species-level accuracy greatly exceeds the resolution of traditional wood identifcation. Conclusion: The end-to-end trained image classifers that we present discriminate the woods based on digital images of the transverse surface of solid wood blocks, which are surfaces and images that can be prepared and cap- tured in the feld. Hence this work represents a strong proof-of-concept for using computer vision and convolutional neural networks to develop practical models for feld screening timber and wood products to combat illegal logging. Keywords: Wood identifcation, Illegal logging, CITES, Forensic wood anatomy, Deep learning, Transfer learning, Convolutional neural networks Background among laboratory scientists, one of the primary road- In the last decade, international interest in combating blocks to meaningful enforcement of these laws is the illegal logging has been on the rise (e.g. the US Lacey Act availability of efcient feld-deployable tools for screen- 2008; the Australian Illegal Logging Prohibition Act 2012; ing timber outside the laboratory [4]. Conceptually sepa- the European Union Timber Regulation 2013; Japan’s rating laboratory-based forensic analysis of specimens Act on Promotion of Distribution and Use of Legally submitted as evidence and feld-screening of wood and Logged Wood Products 2016) as has interest in forensic wood products at ports and border crossings is central methods to support them [1–3]. Although emphasis on to defning the context of the problem to be solved and laboratory-based forensic science is common, especially the degree of specifcity necessary to solve it in a way that is meaningful in the real world. Because feld law *Correspondence: [email protected] enforcement agents are, in most jurisdictions, required to 2 Center for Wood Anatomy Research, USDA Forest Service, Forest establish some form of probable cause to detain or seize Products Laboratory, Madison, WI 53726, USA a shipment of wood, tools intended for feld deployment Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Ravindran et al. Plant Methods (2018) 14:25 Page 2 of 10 should be designed to meet this need efciently [4]. Te pre-trained networks, e.g. ones that have been trained threshold of evidence for probable cause or its interna- on the ImageNet dataset [19]. Convolutional neural net- tional analogs is much lower than forensic-level thresh- works trained on the ImageNet dataset have been shown olds, so tools for feld screening to establish probable to be powerful of-the-shelf feature extractors [20] and cause can provide results with coarser resolution and transfer learning efectively leverages these general pur- lesser certainty than laboratory forensic methods. A typi- pose feature extractors, with parameter fne tuning, and cal feld screening evaluates the veracity of a claim on a permits the use of smaller application-specifc datasets import-export form or shipping manifest. For example, a for training powerful classifers. Successfully developing shipping manifest may claim that the wood is Khaya but a feld-deployable computer vision model for commercial a feld agent determines that the wood is anatomically wood species that are threatened or endangered [e.g. spe- inconsistent with Khaya and in fact is a better match for cies proteted by the Convention on the Trade in Endan- Swietenia and so the shipment could be detained while gered Species (CITES)] is a step toward generating a a specimen is submitted for full laboratory forensic scalable tool for law enforcement to use to combat global analysis. illegal logging. Tis kind of feld screening of wood has historically Te botanical issue of species delimitation is not a mat- been done, if done at all, by human beings with hand ter purely of taxonomy when it comes to illegal logging lenses and keys, atlases of woods, or feld manuals and species conservation through vehicles such as (e.g. [5–10] and others). Such keys are based on the fact CITES. Any law or treaty that identifes and protects that wood structure observed macroscopically shows organisms at the species level necessarily depends on the abundant, characteristic variation typically permitting taxonomic circumscription of those species as a founda- identifcation at the suprageneric or generic level, with tional predicate for defning the protected organisms greater specifcity possible by highly trained experts or by themselves. Te complex interplay of laws for conserva- accessing microscopic characters in the laboratory. tion, taxonomy, species circumscription, and the viability Humans with hand lenses are still the state-of-the-art in of feld-level screening and forensic-level identifcation of the feld in most countries,1 but the time and cost embod- those organisms or their derived products has prompted ied in establishing and maintaining this human-based practical changes to species protection levels in CITES biological domain knowledge, and the variability of skill (e.g. the promotion of Swietenia macrophylla to be at the and accuracy among those applying such knowledge, same protection level as Swietenia mahagoni and Swiete- means this approach is difcult to scale up to keep pace nia humilis in 20032). Prior to this elevation, unscrupu- with increased international interest in and demand for lous traders had the ability to claim a shipment was the feld screening of timber and other wood products. less-protected species and forensics could not prove Computer vision has the potential to provide a practi- otherwise. cal and cost efective way to replace human-based bio- In a real-world practical context, not all woods can or logical domain knowledge for feld screening of wood in need to be identifed to the species level. For example, the trade. One of the primary advantages of this potential is trade name African mahogany includes several species of the ability to generate reproducible identifcations not Khaya that are frequently sold interchangeably under this dependent on individual human training [11], as long as trade name and separating them at the species level may sufcient images of the woods in question are available not be meaningful in trade—the more important ques- for training classifers and can be captured in the feld. tion is likely to be whether they are Khaya or the genuine In computer vison terms, the problem of image-based mahogany genus, Swietenia. Figure 1 shows a “confusion wood identifcation is one of texture-based image clas- cladogram”, a depiction of the expected nested likelihoods sifcation [12, 13]. Convolutional neural networks have of woods (at the genus level) that could be confused with achieved state-of-the-art [14–17] results for image clas- each other based on traditional hand lens wood identica- sifcation in the past few years. While in general convolu- tion. Te relative anatomical distinctness of each genus tional neural networks require large datasets (historically (vertical axis) and the relative variability within the genus not readily available in the context of wood identifca- (extent of the black bars along the horizontal axis) are pro- tion), transfer learning [18] (“Methods” section) provides vided as representations of traditional wood identifcation a pathway to train competitive image classifcation domain knowledge. Based on the relationships