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Search Results
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2023
Journal article
Envisaging a global infrastructure to exploit the potential of digitised collections.
Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it... -
2023
Journal article
Machine learning enhances prediction of plants as potential sources of antimalarials.
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2022
Journal article
Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity.
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2022
Journal article
Improved wood species identification based on multi-view imagery of the three anatomical planes.
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2022
Journal article
Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.
The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence...Machine learning, Neural network, Species richness, Biodiversity, Plant, Australia, Deep learning, and Diversity pattern
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2022
Journal article
Improved Wood Species Identification Based On Multi-View Imagery of The Three Anatomical Planes [PREPRINT].
The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material...Machine learning, Machine vision, Wood anatomical cross-sections, Texture analysis, and Wood species identification
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2021
Journal article
Evidence-based guidelines for developing automated conservation assessment methods [PREPRINT].
Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species have significant gaps in coverage of some taxonomic groups. Automated assessment (AA) methods are gaining popularity to fill these gaps, leveraging improvements in computing and digitally-available information. Choices made...Walker, Barnaby E. ; Leão, Tarciso C.C. ; Bachman, Steven P. ; Lucas, Eve ; Nic Lughadha, Eimear
Biodiversity conservation, Automation, Machine learning, IUCN Red List, and Conservation assessments
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2021
Journal article
Species in lichen-forming fungi: balancing between conceptual and practical considerations, and between phenotype and phylogenomics.
Lichens are symbiotic associations resulting from interactions among fungi (primary and secondary mycobionts), algae and/or cyanobacteria (primary and secondary photobionts), and specific elements of the bacterial microbiome associated with the lichen thallus. The question of what is a species, both concerning the lichen as a whole and its main fungal... -
2022
Journal article
Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.
The mobilization of large-scale datasets of specimen images and metadata through herbarium digitization provide a rich environment for the application and development of machine learning techniques. However, limited access to computational resources and uneven progress in digitization, especially for small herbaria, still present barriers to the wide adoption of these...Walker, Barnaby E. ; Tucker, Allan ; Nicolson, Nicky
Machine learning, Natural history collections, Digitized herbarium specimens, Deep learning, and Computer vision
