Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 5, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

24.9K

Fine-Grained Recognition of Insect Pests from Digital Images: A Survey.

Telmo De Cesaro Júnior1,2, Claudio André Lopes de Oliveira3, Douglas Lau4

  • 1Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Passo Fundo, RS, Brazil. telmojunior@ifsul.edu.br.

Neotropical Entomology
|May 4, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Aphids and their parasitoids persist using temporal pairing and synchrony.

Environmental entomology·2025
Same author

Citrus Bright Spot Virus: A New Dichorhavirus, Transmitted by <i>Brevipalpus azores</i>, Causing Citrus Leprosis Disease in Brazil.

Plants (Basel, Switzerland)·2023
Same author

Historical and Contemporary Perspectives on the Biological Control of Aphids on Winter Cereals by Parasitoids in South America.

Neotropical entomology·2022
Same author

AnemiaAR: a serious game to support teaching of haematology.

Journal of visual communication in medicine·2022
Same author

An Electronic Health Platform for Monitoring Health Conditions of Patients With Hypertension in the Brazilian Public Health System: Protocol for a Nonrandomized Controlled Trial.

JMIR research protocols·2020
Same author

Usability Evaluation Methods for Gesture-Based Games: A Systematic Review.

JMIR serious games·2016

Automated insect identification using computer vision in traps enhances pest management. This review highlights advancements in artificial intelligence for accurate, scalable insect counting, crucial for integrated pest management strategies.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Ecology

Background:

  • Effective pest management relies on accurate, continuous insect monitoring for population dynamics and integrated pest management (IPM) strategies.
  • Manual insect identification and counting are time-consuming, require specialized taxonomic knowledge, and limit scalability.
  • Automation using computer vision and artificial intelligence (AI) offers potential cost reduction, increased accuracy, and scalable analysis for pest monitoring.

Purpose of the Study:

  • To systematically review literature on applied computing solutions for insect identification and counting using digital images.
  • To identify trends and advancements in AI and computer vision for automated pest monitoring in agriculture and ecology.
  • To explore opportunities for integrating automated monitoring with forecasting for enhanced IPM.
Keywords:
Electronic trap, Integrated pest management, Computer vision, Agricultural entomology

More Related Videos

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

7.3K
A Rapid Method to Confine and Safely Handle Bees in the Field
03:44

A Rapid Method to Confine and Safely Handle Bees in the Field

Published on: August 23, 2024

2.0K

Related Experiment Videos

Last Updated: May 5, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

24.9K
A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

7.3K
A Rapid Method to Confine and Safely Handle Bees in the Field
03:44

A Rapid Method to Confine and Safely Handle Bees in the Field

Published on: August 23, 2024

2.0K

Main Methods:

  • Systematic literature review across multidisciplinary and specialized databases (Scopus, ACM, Web of Science, etc.).
  • Focus on studies published between 2020-2025 at the intersection of agriculture, ecology, and computer science.
  • Selection of 57 studies applying computing solutions for insect identification/counting from digital images (traps or in situ).

Main Results:

  • Advancements in convolutional neural networks (CNNs), visual transformers, and attention mechanisms enable multi-species and fine-grained insect recognition.
  • Electronic traps show promise for real-time data collection in pest monitoring.
  • Significant progress in automated insect identification and counting using AI techniques.

Conclusions:

  • AI-driven computer vision significantly improves automated insect identification and counting for pest management.
  • Opportunities exist to leverage microscopy and improve electronic trap network deployment for large-scale monitoring.
  • Integrating real-time data with weather-based forecasting models can establish effective early warning systems for IPM.