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

You might also read

Related Articles

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

Sort by

Same author

Parallel adaptive responses to postponed reproduction increase lifespan and immune defense.

bioRxiv : the preprint server for biology·2026

Same author

Feasibility, acceptability and usability of a thermolabile drugs storage monitoring system at patient's home: a pilot study.

Scientific reports·2026

Same author

Diversity of obligate ectoparasites and parasitism patterns in wild birds of the Balearic Islands: new chewing lice records for Spain.

Parasitology·2025

Same author

Integrating Citizen Science and Field Sampling into Next-Generation Early-Warning Systems for Vector Surveillance: Twenty Years of Municipal Detections of <i>Aedes</i> Invasive Mosquito Species in Spain.

Insects·2025

Same author

Investigating the role of stable flies (Stomoxys calcitrans) and biting midges of the genus Culicoides as potential mechanical vectors of African swine fever virus in Lithuania, Poland and Romania.

Parasites & vectors·2025

Same author

A Self-Configurable BUS Network Topology Based on LoRa Nodes for the Transmission of Data and Alarm Messages in Power Line-Monitoring Systems.

Sensors (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: Jul 12, 2025

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

Remote fruit fly detection using computer vision and machine learning-based electronic trap.

Miguel Molina-Rotger¹, Alejandro Morán¹, Miguel Angel Miranda^2,3

¹Industrial Engineering and Construction Department, University of the Balearic Islands, Palma, Spain.

Frontiers in Plant Science

|October 26, 2023

Summary

This study introduces an electronic trap using machine learning (ML) for precise olive fly detection. Combining Random Forest and Support Vector Machine algorithms achieved high accuracy, aiding sustainable pest management in precision agriculture.

Keywords:

computer vision edge computing machine learning olive fruit fly pest precision agriculture random forest remote sensing support vector machine

More Related Videos

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Published on: July 28, 2023

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Related Experiment Videos

Last Updated: Jul 12, 2025

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Published on: July 28, 2023

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Area of Science:

Agricultural Science
Computer Science
Data Science

Background:

Precision agriculture requires intelligent monitoring systems for pest management.
Computer vision and AI are crucial for early pest detection, like the olive fly.
Limited data availability poses challenges for state-of-the-art Deep Learning techniques.

Purpose of the Study:

To examine olive fly detection and classification using Random Forest (RF) and Support Vector Machine (SVM) algorithms.
To implement these algorithms in an electronic trap system powered by a Raspberry Pi B+ board.
To address data scarcity issues in pest detection using machine learning.

Main Methods:

Utilized Random Forest (RF) and Support Vector Machine (SVM) algorithms for image classification.
Developed an electronic trap system incorporating a Raspberry Pi B+ board for real-time data collection.
Investigated the combined application of RF and SVM to enhance classification accuracy with limited training data.

Main Results:

A combined RF-SVM approach achieved 89.1% accuracy for olive fly detection.
Individual algorithm performance showed 94.5% accuracy for SVM and 91.9% for RF when distinguishing fly species from other insects.
Successfully demonstrated the feasibility of ML-based image classification on small IoT devices.

Conclusions:

The study successfully implemented a machine learning-based electronic trap for effective olive fly detection.
The use of small IoT devices for image classification offers significant potential for resource optimization and privacy.
Scalability through networked traps promises continuous data acquisition and improved accuracy for sustainable fly population management.