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

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

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

Sort by

Same author

Neoadjuvant leukocyte interleukin injection immunotherapy improves overall survival in low-risk locally advanced head and neck squamous cell carcinoma -the <i>IT-MATTERS</i> study.

Pathology oncology research : POR·2025

Same author

Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data.

Sensors (Basel, Switzerland)·2024

Same author

Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing.

Sensors (Basel, Switzerland)·2024

Same author

The relationship between working in the "gig" economy and perceived subjective well-being in Western Balkan countries.

Frontiers in psychology·2023

Same author

Antimicrobial Activity of Selected Essential Oils against Selected Pathogenic Bacteria: In Vitro Study.

Antibiotics (Basel, Switzerland)·2021

Same author

Semiconductor Gas Sensors: Materials, Technology, Design, and Application.

Sensors (Basel, Switzerland)·2020

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 27, 2025

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Prediction of Pest Insect Appearance Using Sensors and Machine Learning.

Dušan Marković¹, Dejan Vujičić², Snežana Tanasković¹

¹Faculty of Agronomy in Čačak, University of Kragujevac, Cara Dušana 34, 32102 Čačak, Serbia.

Sensors (Basel, Switzerland)

|July 24, 2021

Summary

This summary is machine-generated.

This study introduces a machine learning model to predict pest insect appearance using temperature and humidity data. The enhanced model improves prediction accuracy, aiding farmers in timely pest management and resource conservation.

Keywords:

machine learning pest insect appearance precision agriculture temperature and relative humidity sensors

More Related Videos

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

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

Published on: January 9, 2019

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

Related Experiment Videos

Last Updated: Oct 27, 2025

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

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

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

Published on: January 9, 2019

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

Area of Science:

Agricultural Science
Machine Learning
Entomology

Background:

Pest insects cause significant crop yield loss.
Traditional pest monitoring is labor-intensive.
Early detection of pests like Helicoverpa armigera is crucial.

Purpose of the Study:

Develop a machine learning model for daily pest insect prediction.
Incorporate weather parameters like temperature and humidity.
Improve prediction accuracy and reduce false detections.

Main Methods:

Utilized sensor devices with cameras for image analysis of insect traps.
Applied various machine learning classification algorithms.
Extended the model to consider 3- and 5-day periods for prediction.

Main Results:

Initial model achieved up to 76.5% accuracy in predicting insect occurrence.
The extended 5-day period model reached 86.3% accuracy.
The extended model significantly reduced the percentage of false detections.

Conclusions:

Machine learning models can accurately predict pest insect appearance.
Integrating weather data and historical periods enhances prediction.
The proposed model offers a valuable tool for farmers to optimize pest management strategies.