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

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

VEBrant: a multicenter, phase II study of MET inhibitor vebreltinib combined with PD-1-based immunotherapy in advanced clear cell sarcoma.

Future oncology (London, England)·2026

Same author

Factors influencing the transport and transformation of per- and polyfluoroalkyl substances in groundwater systems.

Environmental research·2026

Same author

Random forest imputation and genomic prediction for missing egg production time-series data in yellow-feathered broiler breeders.

BMC genomics·2026

Same author

Design, synthesis and pharmacological evaluation of novel 2-aryloxazole-based H<sub>3</sub> receptor antagonists as potent, antiseizure agents.

Molecular diversity·2026

Same author

Changes in Plant Nitrogen Resorption During Restoration in Inner Mongolia, China.

Plants (Basel, Switzerland)·2026

Same author

Chromosome level genome assembly of the tea ash wood moth, Agriophara rhombata.

Scientific data·2026

Search research articles

Related Experiment Video

Updated: Aug 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional

Yufei Ren¹, Yikang Huang¹, Yichen Wang¹

¹College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

Animals : an Open Access Journal From MDPI

|September 23, 2022

Summary

Accurate chicken age detection is crucial for artificial rearing. An attention encoder structure significantly improved chicken age detection accuracy to 95.2%, aiding poultry farming.

Keywords:

artificial intelligence application attention encoder chick day-age classification convolutional neural network edge computation precision livestock

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Related Experiment Videos

Last Updated: Aug 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Area of Science:

Computer Vision
Artificial Intelligence
Poultry Science

Background:

Artificial rearing of animals is increasingly feasible due to advancements in computer vision and AI.
Accurate chicken day-age detection is vital for optimizing chicken rearing processes.

Purpose of the Study:

To enhance the accuracy of chicken day-age detection using a novel attention encoder structure.
To address challenges posed by imbalanced datasets in chicken age detection.

Main Methods:

Proposed an attention encoder structure for extracting chicken image features.
Employed data augmentation techniques including Cutout, CutMix, and MixUp to handle dataset imbalance.
Integrated the attention encoder into various Convolutional Neural Network (CNN) architectures for comparative analysis and ablation studies.

Main Results:

The attention encoder structure, when applied to ResNet-50, achieved a chicken age detection accuracy of 95.2%.
Data enhancement schemes effectively validated the performance of the proposed attention encoder.
A comprehensive image acquisition system and a mobile detection application were developed.

Conclusions:

The proposed attention encoder structure is effective in improving chicken age detection accuracy.
The developed system and application offer practical solutions for real-world poultry farming scenarios.
This research contributes to the advancement of AI applications in precision agriculture.