Abstract
BackgroundThe current state-of-the-art for field wood identification to combat illegal logging relies on experienced practitioners using hand lenses, specialized identification keys, atlases of woods, and field manuals. Accumulation of this expertise is time-consuming and access to training is relatively rare compared to the international demand for field wood identification. A reliable, consistent and cost effective field screening method is necessary for effective 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.ResultsWe present highly effective computer vision classification models, based on deep convolutional neural networks, trained via transfer learning, to identify the woods of 10 neotropical species in the family Meliaceae, including CITES-listed Swietenia macrophylla, Swietenia mahagoni, Cedrela fissilis, 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. Misclassified images are attributed to classes consistent with traditional wood anatomical results, and our species-level accuracy greatly exceeds the resolution of traditional wood identification.ConclusionThe end-to-end trained image classifiers 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 captured in the field. Hence this work represents a strong proof-of-concept for using computer vision and convolutional neural networks to develop practical models for field screening timber and wood products to combat illegal logging.
Highlights
The current state-of-the-art for field wood identification to combat illegal logging relies on experienced practitioners using hand lenses, specialized identification keys, atlases of woods, and field manuals
In the last decade, international interest in combating illegal logging has been on the rise as has interest in forensic methods to support them [1,2,3]
In this study we report on highly effective computervision classification models, based on deep convolutional neural networks trained via transfer learning, to identify 10 neotropical species in the family Meliaceae, including CITES-listed species Swietenia macrophylla, Swietenia mahagoni, Cedrela fissilis, and Cedrela odorata [7]
Summary
The current state-of-the-art for field wood identification to combat illegal logging relies on experienced practitioners using hand lenses, specialized identification keys, atlases of woods, and field manuals. A shipping manifest may claim that the wood is Khaya but a field agent determines that the wood is anatomically inconsistent with Khaya and is a better match for Swietenia and so the shipment could be detained while a specimen is submitted for full laboratory forensic analysis. This kind of field screening of wood has historically been done, if done at all, by human beings with hand lenses and keys, atlases of woods, or field manuals Humans with hand lenses are still the state-of-the-art in the field in most countries, but the time and cost embodied in establishing and maintaining this human-based biological domain knowledge, and the variability of skill and accuracy among those applying such knowledge, means this approach is difficult to scale up to keep pace with increased international interest in and demand for field screening of timber and other wood products
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