Going deeper in the automated identification of Herbarium specimens

Abstract

BackgroundHundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field.ResultsIn this work, we propose to study and evaluate the accuracy with which herbarium images can be potentially exploited for species identification with deep learning technology. In addition, we propose to study if the combination of herbarium sheets with photos of plants in the field is relevant in terms of accuracy, and finally, we explore if herbarium images from one region that has one specific flora can be used to do transfer learning to another region with other species; for example, on a region under-represented in terms of collected data.ConclusionsThis is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria. Results show the potential of Deep Learning on herbarium species identification, particularly by training and testing across different datasets from different herbaria. This could potentially lead to the creation of a semi, or even fully automated system to help taxonomists and experts with their annotation, classification, and revision works.

Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras

“…Carranza‐Rojas et al. (, ) reported the first attempts to use deep learning to tackle the difficult task of identifying species in large natural history collections and showed that convolutional neural networks trained on thousands of digitized herbarium sheets are able to learn highly discriminative patterns from pressed and dried specimens. These results are very promising for extracting a broad range of other expert annotations in a fully automated way.…”

Machine learning for image based species identification

“…As a first publication in this direction, Carranza‐Rojas, Goeau, Bonnet, Mata‐Montero, and Joly () apply deep learning methods to large herbarium image datasets. In total, more than 260,000 scans of herbarium sheets representing more than 1,204 species were analysed.…”

Automatic vegetation identification in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests

“…Recent research show that deep learning technique successfully detect plant disease or correctly classify the plant specimens in herbarium (Mohanty et al, 2016;Ramcharan et al, 2017;Carranza-Rojas et al, 2017). Deep learning is also a promising technology in the field of remote sensing because it has a natural ability to effectively encode spectral and spatial information (Yue et al, 2015;Nogueira et al, 2017), but application is not All rights reserved.…”