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Developing deep learning models to automate rosewood tree species identification for CITES designation and implementation

Tuo He 1 , 2 , Yang Lu 1 , 2 , Lichao Jiao 1 , 2 , Yonggang Zhang 1 , 2 , Xiaomei Jiang 1 , 2 , and Yafang Yin 1 , 2
  • 1 Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, 100091, Beijing, China
  • 2 Wood Collections (WOODPEDIA), Chinese Academy of Forestry, 100091, Beijing, China
Tuo He
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China
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, Yang Lu
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China
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, Lichao Jiao
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China
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, Yonggang Zhang
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China
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, Xiaomei Jiang
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China
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and Yafang Yin
  • Corresponding author
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China
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Abstract

The implementation of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) to combat illegal logging and associated trade necessitates accurate and efficient field screening of wood species. In this study, a total of 10,237 images of 15 Dalbergia and 11 Pterocarpus species were collected from the transverse surfaces of 417 wood specimens. Three deep learning models were then constructed, trained, and tested with these images to discriminate between timber species. The optimal parameters of the deep learning model were analyzed, and the representative wood anatomical features that were activated by the deep learning models were visualized. The results demonstrated that the overall accuracies of the 26-class, 15-class, and 11-class models were 99.3, 93.7, and 88.4%, respectively. It is suggested that at least 100 high-quality images per species with minimum patch sizes of 1000 × 1000 from more than 10 wood specimens were needed to train reliable and applicable deep learning models. The feature visualization indicated that the vessel groupings and axial parenchyma were the main wood anatomical features activated by the deep learning models. The combination of the state-of-the-art deep learning models, parameter configuration, and feature visualization provide a time- and cost-effective tool for the field screening of wood species to support effective CITES designation and implementation.

  • Barlow, J., Lennox, G. D., Ferreira, J., Berenguer, E., Lees, A. C., Nally, R. M., Thomson, J. R., Ferraz, S. F. B., Louzada, J., Oliveira, V. H. F., et al. (2016). Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535: 144–147, https://doi.org/10.1038/nature18326.

    • Crossref
    • PubMed
    • Export Citation
  • Barrett, M. A., Brown, J. L., Morikawa, M. K., Labat, J., and Yoder, A. D. (2010). CITES designation for endangered rosewood in Madagascar. Science 328: 1109–1110, https://doi.org/10.1126/science.1187740.

    • Crossref
    • PubMed
    • Export Citation
  • Bogucki, R., Cygan, M., Khan, C. B., Klimek, M., Milczek, J. K., and Mucha, M. (2018). Applying deep learning to right whale photo identification. Conserv. Biol. 33: 676–684, https://doi.org/10.1111/cobi.13226.

    • PubMed
    • Export Citation
  • Brancalion, P. H. S., Almeida, D. R. A., Vidal, E., Molin, P. G., Sontag, V. E., Souza, S. E., and Schulze, M. D. (2018). Fake legal logging in the Brazilian Amazon. Science Advances 4: eaa1192, https://doi.org/10.1126/sciadv.aat1192.

    • PubMed
    • Export Citation
  • Canteiro, C., Barcelos, L., Filardi, F., Forzza, R., Green, L., Lanna, J., Leitman, P., Milliken, W., Morim, M. P., Patmore, K., et al. (2019). Enhancement of conservation knowledge through increased access to botanical information. Conserv. Biol. 33: 523–533. https://doi.org/10.1111/cobi.13291.

    • Crossref
    • PubMed
    • Export Citation
  • Cerardo, C., Ehrilich, P. R., Barnosky, A. D., Garcia, A., Pringle, R. M., and Palmer, T. M. (2015). Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci. Adv. 1: e1400253, https://doi.org/10.1126/sciadv.1400253.

    • PubMed
    • Export Citation
  • CITES. (2019). Decisions made on proposals to amend Appendices I and II at CoP18. Available at: https://www.cites.org/eng/updates_decisions_cop18_species_proposals (Accessed 12 September 2019).

  • Dormontt, E. E., Boner, M., Braun, B., Breulmann, G., Degen, B., Espinoza, E., Garden, S., Guilley, P., Hermanson, J. C., Koch, G., et al. (2015). Forensic timber identification: it's time to integrate disciplines to combat illegal logging. Biol. Conserv. 191: 790–798, https://doi.org/10.1016/j.biocon.2015.06.038.

    • Crossref
    • Export Citation
  • Dyrmann, M., Karstoft, H., and Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosyst. Eng. 151: 72–80, https://doi.org/10.1016/j.biosystemseng.2016.08.024.

    • Crossref
    • Export Citation
  • Dumenu, W. K. (2019). Assessing the impact of felling/export ban and CITES designation on exploitation of African rosewood (Pterocarpus erinaceus). Biol. Conserv. 236: 124–133, https://doi.org/10.1016/j.biocon.2019.05.044.

    • Crossref
    • Export Citation
  • Ellwood, E. R., Soltis, P. S., and Klein, M. L. (2019). Conservation Focus: New insights for conservation from expansion of physical-collection digital data. Conserv. Biol. 33: 498–499. https://doi.org/10.1111/cobi.13287.

  • Espinoza, E. O., Wiemann, M. C., Barajas-Morales, J., Chavarria, G. D., and McClure, P. J. (2015). Forensic analysis of CITES-protected Dalbergia timber from the Americas. IAWA J. 36: 311–325, https://doi.org/10.1163/22941932-20150102.

    • Crossref
    • Export Citation
  • Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature 542: 115–118, https://doi.org/10.1038/nature21056.

    • Crossref
    • PubMed
    • Export Citation
  • Figueroa-Mata, G., Mata-Montero, E., Valverde-Ot´arola, J. C., and Arias-Aguilar, D. (2018). Automated image-based identification of forest species: challenges and opportunities for 21st century xylotheques. International Work Conference on Bioinspired Intelligence. IEEE, San Carlos, Costa Rica, https://doi.org/10.1109/IWOBI.2018.8464206.

  • Filho, P. L. P., Oliveira, L. S., Nisgoski, S., and Britto, A. S. (2014). Forest species recognition using macroscopic images. Mach. Vis. Applic. 25: 1019–1031, https://doi.org/10.1007/s00138-014-0592-7.

    • Crossref
    • Export Citation
  • Gasson, P. (2011). How precise can wood identification be? Wood anatomy's role in support of the legal timber trade, especially CITES. IAWA J. 32: 137–154, https://doi.org/10.1163/22941932-90000049.

    • Crossref
    • Export Citation
  • Gasson, P., Miller, R., Stekel, D. J., Whinder, F., and Ziemińska, K. (2010). Wood identification of Dalbergia nigra (CITES Appendix I) using quantitative wood anatomy, principal components analysis and Naïve Bayes classification. Ann. Bot. 105: 45–56, https://doi.org/10.1093/aob/mcp270.

    • Crossref
    • PubMed
    • Export Citation
  • Hartvig, I., Czako, M., Kjær, E. D., Nielsen, L. R., and Theilade, I. (2015). The use of DNA barcoding in identification and conservation of rosewood (Dalbergia spp.) PLoS One 10: e0138231, https://doi.org/10.1371/journal.pone.0138231.

    • PubMed
    • Export Citation
  • He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, USA, pp. 770–778.

  • He, T., Jiao, L., Wiedenhoeft, A. C., and Yin, Y. (2019). Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood. Planta 249: 1617–1625, https://doi.org/10.1007/s00425-019-03116-3.

    • Crossref
    • PubMed
    • Export Citation
  • Houghton, R. A., Byers, B., and Nassikas, A. A. (2015). A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Change 5: 1022–1023, https://doi.org/10.1038/nclimate2869.

    • Crossref
    • Export Citation
  • Hwang, S. W., Kobayashi, K., Zhai, S., and Sugiyama, J. (2018). Automated identification of Lauraceae by scale-invariant feature transform. J. Wood Sci. 64: 69–77, https://doi.org/10.1007/s10086-017-1680-x.

    • Crossref
    • Export Citation
  • IAWA Committee. (2016). Index Xylariorum 4.1. Available at: https://www.iawawebsite.org/uploads/soft/Abstracts/Index%20Xylariorum%204.1.pdf.

  • Irwin, A. (2019). Cops and Loggers: innovative technologies could turn the tide on illegal logging. Nature 568: 19–21. Available at: https://media.nature.com/original/magazine-assets/d41586-019-01035-7/d41586-019-01035-7.pdf (Accessed 19 October 2019).

  • Jiao, L., Yu, M., Wiedenhoeft, A. C., He, T., Li, J., Liu, B., Jiang, X., and Yin, Y. (2018). DNA barcode authentication and library development for the wood of six commercial Pterocarpus species: the critical role of Xylarium specimens. Sci. Rep. 8: 1945, https://doi.org/10.1038/s41598-018-20381-6.

    • Crossref
    • PubMed
    • Export Citation
  • Koch, G., Haag, V., Heinz, I., Richter, H., and Schmitt, U. (2015). Control of international traded timber-the role of macroscopic and microscopic wood identification against illegal logging. J. Forensic Res. 6: 317, https://doi.org/10.4172/2157-7145.1000317.

  • Kovashka, A., Russakovsky, O., Li, F., and Grauman, K. (2016). Crowdsourcing in computer vision. Found. Trends Comput. Graphics Vis. 10: 177–243, https://doi.org/10.1561/0600000071.

    • Crossref
    • Export Citation
  • Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems. NIPS, Lake Tahoe, pp. 1097–1105.

  • Laurance, W. F., Wang, G., Innes, J.L., Wu, S. W., Dai, S., and Lei, J. (2008). The need to cut China's illegal timber imports. Science 319: 1184–1185, https://doi.org/10.1126/science.319.5867.1184b.

    • Crossref
    • PubMed
    • Export Citation
  • LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521: 436–444, https://doi.org/10.1038/nature14539.

    • Crossref
    • PubMed
    • Export Citation
  • Lee, S. H., Chan, C. S., Wilkin, P., and Remagnino, P. (2015). Deep-plant: plant identification with convolutional neural networks. IEEE International Conference on Image Processing (ICIP). IEEE, Quebec City, Canada, pp. 452–456.

  • Lewis, S. L., Edwards, D. P., and Galbraith, D. (2015). Increasing human dominance of tropical forests. Science 349: 827–832, https://doi.org/10.1126/science.aaa9932.

    • Crossref
    • PubMed
    • Export Citation
  • Lim, C. L., Prescott, G. W., Alban, J. D. T., Ziegler, A. D., and Webb, E. L. (2017). Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv. Biol. 31: 1362–1372, https://doi.org/10.1111/cobi.12984.

    • Crossref
    • PubMed
    • Export Citation
  • Lowe, A. J., Dormontt, E. E., Bowie, M. J., Degen, B., Gardner, S., Thomas, D., Clarke, C., Rimbawanto, A., Wiedenhoeft, A., Yin, Y., et al. (2016). Opportunities for improved transparency in the timber trade through scientific verification. Bioscience 66: 990–998, https://doi.org/10.1093/biosci/biw129.

    • Crossref
    • Export Citation
  • Mabberley, D. J. (2009). Mabberley's plant-book: a portable dictionary of plants, their classification and uses. 3rd ed. Cambridge University Press, Cambridge.

  • Martins, J., Oliveira, L. S., Nisgoski, S., and Sabourin, R. (2013). A database for automatic classification of forest species. Mach. Vis. Applic. 24: 567–578, https://doi.org/10.1007/s00138-012-0417-5.

    • Crossref
    • Export Citation
  • Ng, K. K. S., Lee, S. L., Tnah, L. H., Nurul-Farhanah, Z. N., Ng, C. H., Lee, C. T., Tani, N., Diway, B., Lai, P. S., and Khoo, E. (2016). Forensic timber identification: a case study of a CITES listed species, Gonystylus bancanus (Thymelaeaceae). Forensic Sci. Int. Genet. 23: 197–209, https://doi.org/10.1016/j.fsigen.2016.05.002.

    • Crossref
    • PubMed
    • Export Citation
  • Nualart, N., Ibáñez, N., Soriano, I., and López-Pujol, J. (2017). Assessing the relevance of herbarium collections as tools for conservation biology. Bot. Rev. 83: 303–325, https://doi.org/10.1007/s12229-017-9188-z.

    • Crossref
    • Export Citation
  • Pavlovich, M. J., Musselman, B., and Hall, A. B. (2016). Direct analysis in real time-Mass spectrometry (DART-MS) in forensic and security applications. Mass Spectr. Rev. 37: 1–17, https://doi.org/10.1002/mas.21509.

  • Ravindran, P., Costa, A., Soares, R., and Wiedenhoeft, A. C. (2018). Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks. Plant Methods 14: 25, https://doi.org/10.1186/s13007-018-0292-9.

    • Crossref
    • PubMed
    • Export Citation
  • Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen S, et al. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci U.S.A. 108: 9899–9904, 10.1073/pnas.1019576108.

    • Crossref
    • PubMed
    • Export Citation
  • Silva, D. C., Pastore, T. C. M., Soares, L. F., Barros, F. A. S., Bergo, M. C. J., Coradin, V. T. H., Gontijo, A. B., Sosa, M. H., Chacón, C. B., and Braga, J. W. B. (2018). Determination of the country of origin of true mahogany (Swietenia macrophylla King) wood in five Latin American countries using handheld NIR devices and multivariate data analysis. Holzforschung 72: 521–530, https://doi.org/10.1515/hf-2017-0160.

    • Crossref
    • Export Citation
  • Siriwat, P. and Nijiman, V. (2018). Using online media-sourced seizure data to assess the illegal wildlife trade in Siamese rosewood. Environ. Conserv. 45: 352–360, https://doi.org/10.1017/S037689291800005X.

    • Crossref
    • Export Citation
  • Snel, F. A., Braga, J. W. B., Silva, D. C., Wiedenhoeft, A. C., Costa, A., Soares, R., Coradin, V. T. R., and Pastore, T. C. M. (2018). Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES. Wood Sci. Technol. 52: 1411–1427, https://doi.org/10.1007/s00226-018-1027-9.

    • Crossref
    • Export Citation
  • Treanor, N. B. (2015). China's Hongmu Consumption Boom: analysis of the Chinese rosewood trade and links to illegal activity in tropical forested countries. Forest Trends Report Series: Forest Trade and Finance. Available at: https://www.forest-trends.org/ documents/files/doc_5057.pdf (Accessed 12 November 2018).

  • Ugochukwu, A. I., Hobbs, J. E., Phillips, P. W. B., and Kerr, W. A. (2018). Technological solutions to authenticity issues in international trade: the case of CITES listed endangered species. Ecol. Econ. 146: 730–739, https://doi.org/10.1016/j.ecolecon.2017.12.021.

    • Crossref
    • Export Citation
  • Ullman, S., Assif, L., Fetaya, E., and Harari, D. (2016). Atoms of recognition in human and computer vision. Proc. Nat. Acad. Sci. U.S.A. 113: 2744–2749. https://doi.org/10.1073/pnas.1513198113.

    • Crossref
    • Export Citation
  • Wäldchen, J. and Mäder, P. (2018). Plant species identification using computer vision techniques: a systematic literature review. Arch. Comput. Methods Eng. 25: 507–543, https://doi.org/10.1007/s11831-016-9206-z.

    • Crossref
    • PubMed
    • Export Citation
  • Wäldchen, J., Rzanny, M., Seeland, M., and Mäder, P. (2018). Automated plant species identification-trends and future directions. PLoS Comput. Biol. 14: e1005993, https://doi.org/10.1371/journal.pcbi.1005993.

    • PubMed
    • Export Citation
  • Wiedenhoeft, A. C., Simeone, J., Smith, A., Parker-Forney, M., Soares, R., and Fishman, A. (2019). Fraud and misrepresentation in retail forest products exceeds U.S. forensic wood science capacity. PLoS ONE 14: e0219917, https://doi.org/10.1371/journal.pone.0219917.

    • PubMed
    • Export Citation
  • Yu, M., Jiao, L., Guo, J., Wiedenhoeft, A. C., He, T., Jiang, X., and Yin, Y. (2017). DNA barcoding of vouchered xylarium wood specimens of nine endangered Dalbergia species. Planta 246: 1165–1176, https://doi.org/10.1007/s00425-017-2758-9.

    • Crossref
    • PubMed
    • Export Citation
  • Zhang, M., Zhao, G., Liu, B., He, T., Guo, J., Jiang, X., and Yin, Y. (2019). Wood discrimination analyses of Pterocarpus tinctorius and endangered Pterocarpus santalinus using DART-FTICR-MS coupled with multivariate statistics. IAWA J. 40: 58–74, https://doi.org/10.1163/22941932-40190224.

    • Crossref
    • Export Citation
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