Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

References

• Abadi, M. , A. Agarwal , P. Barham , E. Brevdo , Z. Chen , C. Citro , G. S. Corrado , et al. 2016. “Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.” ArXiv Preprint ArXiv : 1603.04467.
   Google Scholar
• Brunken, U. , M. Schmidt , S. Dressler , T. Janßen , A. Thiombiano , and G. Zizka . 2008. “www.westafricanplants.senckenberg.de - an Image-Based Identification Tool for West African Plants.” Taxon 57 (3): 1027–1028.
   Google Scholar
• Carranza-Rojas, J. , H. Goeau , P. Bonnet , E. Mata-Montero , and A. Joly . 2017. “Going Deeper in the Automated Identification of Herbarium Specimens.” BMC Evolutionary Biology 17 (1): 181.10.1186/s12862-017-1014-z
   Web of Science ®Google Scholar
• Clevert, D.-A. , T. Unterthiner , and S. Hochreiter . 2015. “Fast and Accurate Deep Network Learning by Exponential Linear Units (Elus).” ICLR 2016 conference paper. ArXiv Preprint ArXiv : 1511.07289.
   Google Scholar
• Corney, D. P. A. , J. Y. Clark , H. L. Tang , and P. Wilkin . 2012. “Automatic Extraction of Leaf Characters from Herbarium Specimens.” Taxon 61 (1): 231–244.
   Web of Science ®Google Scholar
• Dozat, T. 2016. Incorporating Nesterov Momentum into Adam. ICLR workshop paper, 2016. https://openreview.net/pdf?id=OM0jvwB8jIp57ZJjtNEZ
   Google Scholar
• Dressler, S. , M. Schmidt , and G. Zizka . 2014. “Introducing African Plants - a Photo Guide - an Interactive Identification Tool for Continental Africa.” Taxon 63 (5): 1159–1161.10.12705/635.26
   Google Scholar
• GBIF Secretariat . 2017. “GBIF Backbone Taxonomy.” Accessed via https://www.gbif.org/species/6 in 2017
   Google Scholar
• Givnish, T. J. 1987. “Comparative Studies of Leaf Form: Assessing the Relative Roles of Selective Pressures and Phylogenetic Constraints.” New Phytologist 106 (Suppl.): 131–160.
   Google Scholar
• Goëau, H. , P. Bonnet , and A. Joly . 2017. “Plant Identification Based on Noisy Web Data: The Amazing Performance of Deep Learning (LifeCLEF 2017).” CEUR Workshop Proceedings.
   Google Scholar
• He, K. , X. Zhang , S. Ren , and J. Sun . 2016. “Deep Residual Learning for Image Recognition.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
   Google Scholar
• Hoehndorf, R. , M. Alshahrani , G. V. Gkoutos , G. Gosline , Q. Groom , T. Hamann , J. Kattge , et al . 2016. “The Flora Phenotype Ontology (FLOPO): Tool for Integrating Morphological Traits and Phenotypes of Vascular Plants.” Journal of Biomedical Semantics 7 (1): 309.10.1186/s13326-016-0107-8
   Web of Science ®Google Scholar
• Hubel, D. H. , and T. N. Wiesel . 1968. “Receptive Fields and Functional Architecture of Monkey Striate Cortex.” The Journal of Physiology 195 (1): 215–243.10.1113/jphysiol.1968.sp008455
   PubMed Web of Science ®Google Scholar
• Ioffe, S. , and C. Szegedy . 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” International Conference on Machine Learning , 448–456.
   Google Scholar
• Joly, A. , P. Bonnet , H. Goëau , J. Barbe , S. Selmi , J. Champ , S. Dufour-Kowalski , et al . 2016. “A Look inside the Pl@ntNet Experience.” Multimedia Systems 22 (6): 751–766.10.1007/s00530-015-0462-9
   Web of Science ®Google Scholar
• Kumar, N. , P. N. Belhumeur , A. Biswas , D. W. Jacobs , W. J. Kress , I. C. Lopez , and J. V. B. Soares . 2012. “Leafsnap: A Computer Vision System for Automatic Plant Species Identification.” Computer Vision–ECCV 2012 , 502–516. Berlin, Heidelberg: Springer.10.1007/978-3-642-33709-3
   Google Scholar
• Le Bras, G. , M. Pignal , M. L. Jeanson , S. Muller , C. Aupic , B. Carré , G. Flament , et al . 2017. “The French Muséum National d’Histoire Naturelle Vascular Plant Herbarium Collection Dataset.” Scientific Data 4: 170016.10.1038/sdata.2017.16
   PubMed Web of Science ®Google Scholar
• LeCun, Y. , and Y. Bengio . 1995. “Convolutional Networks for Images, Speech, and Time Series.” The Handbook of Brain Theory and Neural Networks 3361 (10): 276–279.
   Google Scholar
• LeCun, Y. , Y. Bengio , and G. Hinton . 2015. “Deep Learning.” Nature 521 (7553): 436–444.10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Matsugu, M. , K. Mori , Y. Mitari , and Y. Kaneda . 2003. “Subject Independent Facial Expression Recognition with Robust Face Detection Using a Convolutional Neural Network.” Neural Networks 16 (5–6): 555–559.10.1016/S0893-6080(03)00115-1
   PubMed Web of Science ®Google Scholar
• Olshausen, B. A. , and D. J. Field . 1996. “Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images.” Nature 381: 607.10.1038/381607a0
   PubMed Web of Science ®Google Scholar
• Pound, M. P. , A. J. Burgess , M. H. Wilson , J. A. Atkinson , M. Griffiths , A. S. Jackson , A. Bulat , et al. 2017. “Deep Machine Learning Provides State-of-the-Art Performance in Image-Based Plant Phenotyping.” GigaScience 6 (10): 1–10.
   Web of Science ®Google Scholar
• Schmidhuber, J. 2015. “Deep Learning in Neural Networks: An Overview.” Neural Networks 61: 85–117.10.1016/j.neunet.2014.09.003
   PubMed Web of Science ®Google Scholar
• Schuettpelz, E. , P. B. Frandsen , R. B. Dikow , A. Brown , S. Orli , M. Peters , A. Metallo , V. A. Funk , and L. J. Dorr . 2017. “Applications of Deep Convolutional Neural Networks to Digitized Natural History Collections.” Biodiversity Data Journal 5: e21139.10.3897/BDJ.5.e21139
   Web of Science ®Google Scholar
• Smith, G. F. , J. P. Roux , P. Raven , and E. Figueiredo . 2011. “African Herbaria Support Transformation on the Continent 1.” Annals of the Missouri Botanical Garden 98 (2): 272–276.10.3417/2010050
   Web of Science ®Google Scholar
• Thiers, B. 2017. “The World’s Herbaria 2016: A Summary Report Based on Data from Index Herbariorum.” http://sweetgum.nybg.org/science/docs/The World’s Herbaria 2016 18 Jan 2017.pdf
   Google Scholar
• Ubbens, J. R. , and I. Stavness . 2017. “Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks.” Frontiers in Plant Science 8: 1476.10.3389/fpls.2017.01190
   Web of Science ®Google Scholar
• Unger, J. , D. Merhof , and S. Renner . 2016. “Computer Vision Applied to Herbarium Specimens of German Trees: Testing the Future Utility of the Millions of Herbarium Specimen Images for Automated Identification.” BMC Evolutionary Biology 16 (1): 25.10.1186/s12862-016-0827-5
   Web of Science ®Google Scholar
• Van Horn, G. , O. Mac Aodha , Y. Song , A. Shepard , H. Adam , P. Perona , and S. Belongie . 2017. “The iNaturalist Challenge 2017 Dataset.” Retrieved from http://arxiv.org/abs/1707.06642
   Google Scholar
• Yosinski, J. , J. Clune , Y. Bengio , and H. Lipson . 2014. “How Transferable Are Features in Deep Neural Networks?” Advances in Neural Information Processing Systems 3320–3328.
   Google Scholar
• Zeiler, M. D. , and R. Fergus . 2014. “Visualizing and Understanding Convolutional Networks.” In European Conference on Computer Vision , edited by D. Fleet , T. Pajtla , B. Schiele and T. Tuytelaars , 818–833. Cham: Springer.
   Google Scholar