Taxonomic classification of digitized specimens using machine learning
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The document details a project on taxonomic classification of digitized biological specimens using machine learning techniques. It includes case studies on slipper orchids and Javanese butterflies, highlighting the structure and components of the project as well as results from machine learning applications. Future steps involve testing additional classifiers and expanding the platform to classify more taxa.
![Project structure: reference images
photos [table]
id INTEGER NOT NULL
md5sum VARCHAR(32) NOT NULL
path VARCHAR(255)
title VARCHAR(100)
description VARCHAR(255)
photos_tags [table]
photo_id INTEGER NOT NULL
tag_id INTEGER NOT NULL
tags [table]
id INTEGER NOT NULL
name VARCHAR(50) NOT NULL
photos_taxa [table]
photo_id INTEGER NOT NULL
taxon_id INTEGER NOT NULL
taxa [table]
id INTEGER NOT NULL
rank_id INTEGER NOT NULL
name VARCHAR(50) NOT NULL
description VARCHAR(255)
ranks [table]
id INTEGER NOT NULL
name VARCHAR(50) NOT NULL](https://image.slidesharecdn.com/2016-09-20-vos-leben-160920225814/85/Taxonomic-classification-of-digitized-specimens-using-machine-learning-6-320.jpg)
Taxonomic classification of digitized specimens using machine learning
- 1. Taxonomic classification of digitized specimens using machine learning Rutger Vos
- 2. Taxonomic classification1 of digitized specimens2 using machine learning3 1. To give the right taxonomic name to a thing, or at least approximate it to a higher level (e.g. Genus, Family) 2. Photographs of biological objects, e.g. from a natural history collection and taken in a standardized setup 3. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data
- 3. Case study: slipper orchids Slipper orchids • Traded illegally • Photographed “in the wild”
- 4. Case study: Javanese butterflies Van Groenendael-Krijger collection • Collected in the 1930s • Photographed in standardized setup
- 5. Project structure overview • Open source, freely available at: github.com/naturalis • Designed as loosely coupled, swappable modules • Intended for re-use for multiple cases
- 6. Project structure: reference images photos [table] id INTEGER NOT NULL md5sum VARCHAR(32) NOT NULL path VARCHAR(255) title VARCHAR(100) description VARCHAR(255) photos_tags [table] photo_id INTEGER NOT NULL tag_id INTEGER NOT NULL tags [table] id INTEGER NOT NULL name VARCHAR(50) NOT NULL photos_taxa [table] photo_id INTEGER NOT NULL taxon_id INTEGER NOT NULL taxa [table] id INTEGER NOT NULL rank_id INTEGER NOT NULL name VARCHAR(50) NOT NULL description VARCHAR(255) ranks [table] id INTEGER NOT NULL name VARCHAR(50) NOT NULL
- 7. Project structure: image processing Speeded Up Robust Features
- 8. Project structure: machine learning
- 10. Project structure: user interface
- 11. Results: SURF features • PCA plots of the “speeded up robust features” show clustering both at the genus (top) and species (bottom) level • Some species are so dimorphic that the sexes are treated as separate species (not shown) • Some individuals are “gynandromorphic”, though there is likely positive collection bias • Some taxa are much more variable than others
- 12. Results: k-folds cross-validation • Split the data in k (2, 5, 10) partitions • Train on 1 partition, use k-1 as “out-of-sample” data • Count number of correct/incorrect/unknown identifications
- 13. Next steps • Application of trained neural networks to the entire VGKS collection (once that is fully digitized) • Testing other classifiers in addition to ANNs • Improvement of the end user interface, possibly as a native ‘app’ or on the web • Extension of the platform to additional cases, such as shells (snails, bivalves) • Do more with the image feature data: mimicry, character displacement, dimorphism
- 14. Acknowledgements Naturalis sector Collection • Max Caspers • Luc Willemse • Jan Moonen • Digitization volunteers Hogeschool Leiden • Barbara Gravendeel • Patrick Wijntjes • Saskia de Vetter LIACS • Fons Verbeek • Mengke Li • Yuanhao Guo IBL • Wim van Tongeren WUR • Feia Matthijssen Made possible by • Naturalis internal grant for application-oriented research • The Van Groenendael-Krijger Stichting • Kind contributions of photos by numerous orchid breeders