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

Survival Tree01:19

Survival Tree

388
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
388

You might also read

Related Articles

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

Sort by
Same author

MSRA-Net: A Multi-Task Learning Model for Soil Texture Prediction with Dynamic Weighting and Prior Knowledge Soft Constraints.

Sensors (Basel, Switzerland)·2025
Same author

Structural equation model was used to evaluate the effects of soil chemical environment, fertility and enzyme activity on eucalyptus biomass.

Royal Society open science·2023
Same author

Radiocontrast agent and intraductal pressure promote the progression of post-ERCP pancreatitis by regulating inflammatory response, cellular apoptosis, and tight junction integrity.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]·2021
Same author

Occurrence and fate of polycyclic aromatic hydrocarbons from electronic waste dismantling activities: A critical review from environmental pollution to human health.

Journal of hazardous materials·2021
Same author

ATG16L2 overexpression is associated with a good prognosis in colorectal cancer.

Journal of gastrointestinal oncology·2021
Same author

A Novel Pyroptosis-Related Gene Signature for Prognostic Prediction of Head and Neck Squamous Cell Carcinoma.

International journal of general medicine·2021
Same journal

Correction: Sutthanont et al. Effectiveness of Herbal Essential Oils as Single and Combined Repellents Against <i>Aedes aegypti</i>, <i>Anopheles dirus</i> and <i>Culex quinquefasciatus</i> (Diptera: Culicidae). <i>Insects</i> 2022, <i>13</i>, 658.

Insects·2026
Same journal

A Taxonomic Revision of the East Mediterranean Species of the <i>Crematogaster scutellaris</i> Complex (Hymenoptera: Formicidae).

Insects·2026
Same journal

Structural Characteristics for the Interaction of 1-Benzyl-2-Methylbenzimidazoles as Insect Growth Regulators and Juvenile Hormone Binding Protein.

Insects·2026
Same journal

Structure of Epigeic and Arboreal Ant Communities in Forest Fragments Within Agricultural Landscapes of the Brazilian Cerrado.

Insects·2026
Same journal

Insects as an Alternative Protein Source: A Sustainable Approach to Future Food Security.

Insects·2026
Same journal

Carpet Beetle Species (Coleoptera: Dermestidae) in Austrian Heritage Interiors and Their European Distributions.

Insects·2026
See all related articles

Related Experiment Video

Updated: Jan 16, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

488

Lightweight Pest Object Detection Model for Complex Economic Forest Tree Scenarios.

Xiaohui Cheng1,2, Xukun Wang1, Yanping Kang1,2

  • 1College of Computer Science and Engineering, Guilin University of Technology, Guilin 541004, China.

Insects
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces LightFAD-DETR, an efficient AI model for detecting small pests in forests. It improves accuracy and speed for sustainable forest management by addressing detection challenges.

Keywords:
RT-DETReconomic forest pest controlfeature aggregationsmall object detection

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.6K

Related Experiment Videos

Last Updated: Jan 16, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

488
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.6K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Forestry Science

Background:

  • Pest control in economic forests is vital for sustainable management.
  • Current methods struggle with efficiency and detecting small, occluded pests.

Purpose of the Study:

  • To develop a lightweight, high-accuracy AI model for detecting small forest pests.
  • To improve pest detection efficiency and reduce miss rates in complex forest environments.

Main Methods:

  • Proposed LightFAD-DETR, a lightweight architecture based on RT-DETR.
  • Integrated YOLOv9 backbone and a novel feature aggregation diffusion network.
  • Utilized re-parameterization techniques and progressive training for efficiency.

Main Results:

  • LightFAD-DETR achieved a 1.4% mAP improvement over the baseline RT-DETR.
  • Reduced model parameters by 41.7% and computational load by 35.0%.
  • Reached an inference speed of 106.3 FPS, demonstrating balanced accuracy and efficiency.

Conclusions:

  • LightFAD-DETR offers a significant advancement in lightweight pest detection for forest management.
  • The model effectively handles small, occluded objects in complex scenarios.
  • Achieved superior performance with reduced computational resources for edge deployment.