Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Classification and Identification

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Protocol for spatiotemporal analysis of climate-induced negative sentiment in social media.

STAR protocols·2026

Same author

The <i>MV Hondius</i> Warning: Hantavirus Surveillance Blind Spots.

The Lancet regional health. Europe·2026

Same author

Coupling experiments and macroecological models to resolve multi-stressor effects in vector-pathogen systems.

Nature reviews. Microbiology·2026

Same author

Evaluating the ability of a thermal biology-informed reproduction number to explain patterns of West Nile incidence in Europe.

One health (Amsterdam, Netherlands)·2026

Same author

Swedish cattle farmers' preferences for control measures against Mycoplasma bovis: A discrete choice experiment.

Preventive veterinary medicine·2026

Same author

A flexible class of latent variable models for the analysis of antibody response data.

Biostatistics (Oxford, England)·2026

Same journal

Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates.

Scientific reports·2026

Same journal

SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems.

Scientific reports·2026

Same journal

The effect of stretching the pectoralis major, sternocleidomastoid, and iliopsoas muscles on 800 m swimming performance in master swimmers.

Scientific reports·2026

Same journal

ISNR-PQC: isometry noise resilience post quantum cryptography primitive.

Scientific reports·2026

Same journal

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026

Same journal

Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Automated tick classification using deep learning and its associated challenges in citizen science.

Anna Omazic¹, Giulio Grandi², Stefan Widgren³

¹Department of Chemistry, Environment and Feed Hygiene, Swedish Veterinary Agency (SVA), 751 89, Uppsala, Sweden.

Scientific Reports

|July 10, 2025

Summary

This summary is machine-generated.

AI and citizen science can help monitor ticks, including invasive species, to protect public health. Deep learning models show promise for automated tick classification, aiding surveillance efforts.

More Related Videos

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Related Experiment Videos

Last Updated: Sep 16, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Veterinary Entomology
Artificial Intelligence
Public Health

Background:

Lyme borreliosis and tick-borne encephalitis pose significant public health risks in Europe.
Introduction of exotic tick species into Europe necessitates enhanced surveillance and identification methods.

Purpose of the Study:

To develop and evaluate deep learning models for automated tick species classification using citizen-submitted images.
To assess the feasibility of integrating AI with citizen science for large-scale tick monitoring.

Main Methods:

A citizen science initiative collected over 15,000 tick images.
Deep learning models (EfficientNetV2M) were developed using image analysis, object detection, and transfer learning.
Model performance was evaluated on out-of-distribution, citizen-submitted data.

Main Results:

The EfficientNetV2M model achieved a macro recall of 0.60 and an MCC of 0.55.
Demonstrated feasibility of AI-citizen science integration for tick surveillance.
Identified challenges including class imbalance and morphological variability.

Conclusions:

AI-driven citizen science offers a scalable framework for tick surveillance.
The developed system serves as a proof of concept for future AI applications in One Health.
Further development is needed to overcome classification challenges for robust species identification.