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Taxonomic Classification of Ants (Formicidae) from Images using Deep Learning

https://doi.org/10.1101/407452
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00114-012-0884-6openalex-snapshot
A life history continuum in the males of a Neotropical ant assemblage: refuting the sperm vessel hypothesis
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Discrimination of haploid and diploid males of Bombus terrestris (Hymenoptera; Apidae) based on wing shape
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Taxonomic sufficiency in pollution assessment
inferredcites →https://doi.org/10.1016/j.aspen.2012.03.006openalex-snapshot
Identification of butterfly species with a single neural network system
inferredcites →https://doi.org/10.1016/j.biocon.2010.07.034openalex-snapshot
A new method based on taxonomic sufficiency to simplify studies on Neotropical ant assemblages
inferredcites →https://doi.org/10.1016/j.biosystemseng.2011.10.003openalex-snapshot
The identification of butterfly families using content-based image retrieval
inferredcites →https://doi.org/10.1016/j.biosystemseng.2016.08.024openalex-snapshot
Plant species classification using deep convolutional neural network
inferredcites →https://doi.org/10.1016/j.compag.2012.08.008openalex-snapshot
Image-based orchard insect automated identification and classification method
inferredcites →https://doi.org/10.1016/j.cviu.2017.05.007openalex-snapshot
Systematic evaluation of convolution neural network advances on the Imagenet
inferredcites →https://doi.org/10.1016/j.ecoinf.2017.05.005openalex-snapshot
LeafNet: A computer vision system for automatic plant species identification
inferredcites →https://doi.org/10.1016/j.eswa.2017.05.039openalex-snapshot
Deep learning for biological image classification
inferredcites →https://doi.org/10.1016/j.gecco.2016.05.003openalex-snapshot
A quantitative assessment of the vegetation types on the island of St. Eustatius, Dutch Caribbean
inferredcites →https://doi.org/10.1016/j.inffus.2017.02.007openalex-snapshot
Multi-view learning overview: Recent progress and new challenges
inferredcites →https://doi.org/10.1016/j.knosys.2012.03.014openalex-snapshot
A new automatic identification system of insect images at the order level
inferredcites →https://doi.org/10.1016/j.neucom.2015.09.116openalex-snapshot
Deep learning for visual understanding: A review
inferredcites →https://doi.org/10.1016/j.neucom.2015.10.122openalex-snapshot
DeepFish: Accurate underwater live fish recognition with a deep architecture
inferredcites →https://doi.org/10.1016/s0169-5347(00)89026-6openalex-snapshot
The potential for computer-aided identification in biodiversity research
inferredcites →https://doi.org/10.1017/s000748530002736xopenalex-snapshot
Automating the identification of insects: a new solution to an old problem
inferredcites →https://doi.org/10.1017/s1477200003001208openalex-snapshot
Automated identification of live moths (Macrolepidoptera) using digital automated identification System (DAISY)
inferredcites →https://doi.org/10.1023/a:1018348204573openalex-snapshot
Image analysis, neural networks, and the taxonomic impediment to biodiversity studies
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/srep12786openalex-snapshot
A tool for developing an automatic insect identification system based on wing outlines
inferredcites →https://doi.org/10.1046/j.1365-2664.2002.00704.xopenalex-snapshot
Using ants as bioindicators in land management: simplifying assessment of ant community responses
inferredcites →https://doi.org/10.1046/j.1439-0418.1999.00307.xopenalex-snapshot
Automating insect identification: exploring the limitations of a prototype system
inferredcites →https://doi.org/10.1046/j.1442-9993.1999.01003.xopenalex-snapshot
Taxonomic sufficiency in ecological studies of terrestrial invertebrates
inferredcites →https://doi.org/10.1046/j.1523-1739.1996.10010099.xopenalex-snapshot
Invertebrate Morphospecies as Surrogates for Species: A Case Study
inferredcites →https://doi.org/10.1051/apido:2008028openalex-snapshot
Identification of Africanized honey bees through wing morphometrics: two fast and efficient procedures
inferredcites →https://doi.org/10.1098/rspb.2001.1713openalex-snapshot
Biogeographical concordance and efficiency of taxon indicators for establishing conservation priority in a tropical rainforest biota
inferredcites →https://doi.org/10.1098/rstb.2003.1442openalex-snapshot
Automated species identification: why not?
inferredcites →https://doi.org/10.1101/070904openalex-snapshot
OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvprw.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/dicta.2016.7797091openalex-snapshot
Understanding Data Augmentation for Classification: When to Warp?
inferredcites →https://doi.org/10.1109/icip.2015.7350839openalex-snapshot
Deep-plant: Plant identification with convolutional neural networks
inferredcites →https://doi.org/10.1109/icra.2016.7487365openalex-snapshot
Robust single-view instance recognition
inferredcites →https://doi.org/10.1109/tpami.2013.50openalex-snapshot
Representation Learning: A Review and New Perspectives
inferredcites →https://doi.org/10.1111/j.1365-3113.1995.tb00102.xopenalex-snapshot
IDENTIFICATION GUIDE TO THE ANT GENERA OF THE WORLD
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1155/2017/7361042openalex-snapshot
Deep Learning for Plant Identification in Natural Environment
inferredcites →https://doi.org/10.11646/zootaxa.1668.1.26openalex-snapshot
Phylogeny, classification, and species-level taxonomy of ants (Hymenoptera: Formicidae)*
inferredcites →https://doi.org/10.1186/s12862-017-0890-6openalex-snapshot
bModelTest: Bayesian phylogenetic site model averaging and model comparison
inferredcites →https://doi.org/10.1186/s12862-017-1014-zopenalex-snapshot
Going deeper in the automated identification of Herbarium specimens
inferredcites →https://doi.org/10.13031/2013.30344openalex-snapshot
Machine Vision and Image Processing for Plant Identification
inferredcites →https://doi.org/10.1371/journal.pbio.1001176openalex-snapshot
Mouse PRDM9 DNA-Binding Specificity Determines Sites of Histone H3 Lysine 4 Trimethylation for Initiation of Meiotic Recombination
inferredcites →https://doi.org/10.1371/journal.pcbi.1005993openalex-snapshot
Automated plant species identification—Trends and future directions
inferredcites →https://doi.org/10.1525/9780520934559openalex-snapshot
Ants of North America
inferredcites →https://doi.org/10.1525/9780520962996openalex-snapshot
Ants of Africa and Madagascar
inferredcites →https://doi.org/10.3389/fpls.2016.01419openalex-snapshot
Using Deep Learning for Image-Based Plant Disease Detection
inferredcites →https://doi.org/10.3897/bdj.5.e21139openalex-snapshot
Applications of deep convolutional neural networks to digitized natural history collections
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1507.08286openalex-snapshot
Deep Learning for Single-View Instance Recognition
inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
inferredcites →https://doi.org/10.48550/arxiv.1609.04747openalex-snapshot
An overview of gradient descent optimization algorithms
inferredcites →https://doi.org/10.48550/arxiv.1801.00631openalex-snapshot
Deep Learning: A Critical Appraisal
inferredcites →https://doi.org/10.5244/c.28.6openalex-snapshot
Return of the Devil in the Details: Delving Deep into Convolutional Nets
inferredcites →https://doi.org/10.5751/es-00014-010108openalex-snapshot
Using Ants as bioindicators: Multiscale Issues in Ant Community Ecology
inferredcites →https://doi.org/10.5860/choice.32-2110openalex-snapshot
Identification guide to the ant genera of the world

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/njz2.70049openalex-snapshot
Deep Learning Pipeline for the Identification of Ground Beetles (Carabidae) in New Zealand
inferred← citeshttps://doi.org/10.1007/s10462-024-11074-wopenalex-snapshot
Classification of animal species via deep neural networks and species distribution modeling: a systematic review
inferred← citeshttps://doi.org/10.1016/j.ympev.2019.106562openalex-snapshot
A demonstration of unsupervised machine learning in species delimitation
inferred← citeshttps://doi.org/10.1080/00779962.2025.2529169openalex-snapshot
Counting and identifying invertebrates from pitfall traps: a novel method using images and deep learning
inferred← citeshttps://doi.org/10.1093/jee/toag230openalex-snapshot
SolenopsisDetector : development of an automatic detection system for fire ants using computer vision and deep learning
inferred← citeshttps://doi.org/10.1109/sceecs57921.2023.10062974openalex-snapshot
Ant Species Recognition using Convolutional Neural Network
inferred← citeshttps://doi.org/10.1111/2041-210x.13901openalex-snapshot
Deep learning as a tool for ecology and evolution
inferred← citeshttps://doi.org/10.13102/sociobiology.v72i4.11539openalex-snapshot
Surveying Ant Fauna in Northern Espírito Santo, Brazil: New Distribution Records From the Coleção Zoológica Norte Capixaba
inferred← citeshttps://doi.org/10.3390/s23062954openalex-snapshot
Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
inferred← citeshttps://doi.org/10.3897/jhr.95.95964openalex-snapshot
Trialling a convolution neural network for the identification of Braconidae in New Zealand
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