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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model.

Computers in biology and medicine·2024
Same author

Primary Biliary Cholangitis- Autoimmune Hepatitis Overlap Syndrome.

Indian journal of pediatrics·2018
Same journal

Interspecific variation in the fruit infestation level by <i>Anastrepha fraterculus</i> and <i>Ceratitis capitata</i> in northwestern Argentina mirrors the types of land use and host plant origin.

Bulletin of entomological research·2026
Same journal

Modulating effect of plant growth-promoting rhizobacteria on wheat-induced resistance to <i>Schizaphis graminum</i>.

Bulletin of entomological research·2026
Same journal

Molecular monitoring of insecticide resistance in <i>Aphis gossypii</i> Glover (Hemiptera: Aphididae) from different crops in Greece, using novel ddPCR diagnostics.

Bulletin of entomological research·2026
Same journal

Feeding preferences and oviposition performance of olive weevil adult <i>Pimelocerus perforatus</i> (Roelofs, 1873) on five Oleaceae plants.

Bulletin of entomological research·2026
Same journal

Arthropod predator nutrient content changes with wheat sowing period but is not driven by prey availability.

Bulletin of entomological research·2026
Same journal

Age-stage, two-sex life table and seasonal dynamics of <i>Brachycaudus helichrysi</i> on <i>Prunus domestica</i>: implications for pest management in Himalayan plum orchards.

Bulletin of entomological research·2026
See all related articles

Related Experiment Video

Updated: Jul 6, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K

Automated precision beekeeping for accessing bee brood development and behaviour using deep CNN.

Neha Rathore1, Dheeraj Agrawal1

  • 1Department of Electronics and Communication, Maulana Azad National Institute of Technology (MANIT), Bhopal, India.

Bulletin of Entomological Research
|January 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a precision beekeeping method using computer vision to monitor bee brood health. This technology helps reduce bee colony losses and improve honey production through early detection of issues.

Keywords:
bee brood detectioncell classificationcolony collapse disorderimage processingneural network

More Related Videos

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

11.0K
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

5.7K

Related Experiment Videos

Last Updated: Jul 6, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K
Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

11.0K
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

5.7K

Area of Science:

  • Ecology and Environmental Science
  • Agricultural Technology
  • Computer Science

Background:

  • Bee populations are declining globally due to colony collapse disorder, posing ecological risks.
  • Effective apiculture requires monitoring hive health, bee behavior, and brood development.
  • Current methods for monitoring bee health can be labor-intensive and may not provide timely interventions.

Purpose of the Study:

  • To propose a precision beekeeping method utilizing advanced technology to reduce bee colony mortality.
  • To enhance conventional apiculture by analyzing bee colony characteristics through technological tools.
  • To apply digital image processing and computer vision for the identification and analysis of bee brood stages.

Main Methods:

  • Utilizing digital image processing and computer vision techniques for visual analysis of beehive images.
  • Preprocessing images to enhance key features of bee brood.
  • Segmenting and classifying immature bee brood stages using computer vision algorithms.

Main Results:

  • The study successfully applied computer vision for the identification and analysis of various immature bee brood stages.
  • The proposed method was tested and compared against existing methodologies to evaluate its efficiency.
  • The research demonstrated the potential of digital image processing in monitoring brood health and development.

Conclusions:

  • Precision beekeeping, incorporating computer vision, can significantly aid in reducing bee colony mortality.
  • Technological advancements offer a promising approach to improve the understanding and management of bee colonies.
  • This method provides a foundation for developing automated systems for real-time monitoring of apiculture health.