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

Pollination and Flower Structure02:40

Pollination and Flower Structure

73.2K
Flowers are the reproductive, seed-producing structures of angiosperms. Typically, flowers consist of sepals, petals, stamens, and carpels. Sepals and petals are the vegetative flower organs. Stamens and carpels are the reproductive organs.  
73.2K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.5K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.5K

You might also read

Related Articles

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

Sort by
Same author

Effectiveness and the synergism effects of Ipomoea cairica leaf plant extract and Metarhizium anisopliae fungi (Meta-G4) against larvae of Aedes aegypti Linnaeus and Aedes albopictus Skuse (Diptera: Culicidae).

Journal of insect science (Online)·2025
Same author

Assessing AedesTech mosquito home system on yellow fever mosquito Aedes aegypti (Linnaeus) in Northern Malaysia.

Journal of insect science (Online)·2025
Same author

Evaluation of the pathogenicity of endophytic fungi isolated from spines of rattan (Calamus castaneus) against other plant hosts.

Journal of applied microbiology·2022
Same author

Rattan spines as deterrence? A spinescence study on different species of rattans.

Plant signaling & behavior·2020
Same author

School-based health education for dengue control in Kelantan, Malaysia: Impact on knowledge, attitude and practice.

PLoS neglected tropical diseases·2020
Same author

Rattan litter-collecting structures attract nest-building and defending ants.

Plant signaling & behavior·2019

Related Experiment Video

Updated: Nov 1, 2025

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

11.1K

Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees.

Nik Fadzly1, Wan Fatma Zuharah1, Jenny Wong Jenn Ney1

  • 1School of Biological Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia.

Plant Signaling & Behavior
|June 21, 2021
PubMed
Summary

Bee orchids mimic female bees to trick male bees into pollination. However, this study found that artificial intelligence, using machine learning, can accurately distinguish bee orchids from real bees.

Keywords:
Bee orchidsartificial intelligencebeesmachine learningmimicrypseudo-copulation

More Related Videos

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

1.1K
Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea
07:19

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea

Published on: November 25, 2016

11.8K

Related Experiment Videos

Last Updated: Nov 1, 2025

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

11.1K
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

1.1K
Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea
07:19

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea

Published on: November 25, 2016

11.8K

Area of Science:

  • Plant-animal interactions
  • Bio-inspired AI
  • Evolutionary biology

Background:

  • Bee orchids (Ophrys) exhibit dishonest signaling by mimicking female bees.
  • This mimicry facilitates pollination through pseudo-copulation by male bees.
  • Previous studies relied on visual comparisons of floral structures.

Purpose of the Study:

  • To quantitatively assess if machine learning can differentiate bee orchids from bees.
  • To compare the classification capabilities of AI against natural mimicry.
  • To explore the application of machine learning in understanding plant-animal interactions.

Main Methods:

  • Collected 2000 images of bees, wasps, and Ophrys sp. from Google Images.
  • Filtered images to a final selection of 995, using 80% for model training and 20% for testing.
  • Employed Logistic Regression for supervised model building and Wolfram Mathematica for analysis.
  • Utilized the SURF method to identify key structural similarities.

Main Results:

  • The machine learning model achieved excellent image classification accuracy.
  • Metrics including accuracy baseline, mean cross-entropy, and AUC confirmed robust performance.
  • The model successfully distinguished bee orchids from bees, indicating AI is not fooled.
  • SURF method highlighted specific structural resemblances exploited by the orchid.

Conclusions:

  • Artificial intelligence, through machine learning, can effectively distinguish bee orchids from bees.
  • This quantitative approach offers a new perspective on deceptive signaling in nature.
  • Researchers are encouraged to use larger datasets for more advanced AI-driven ecological studies.