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

You might also read

Related Articles

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

Sort by
Same author

Associations Between Fine Particulate Matter-Associated Bacteria and Respiratory Tract Microbiota in Pigs.

Animals : an open access journal from MDPI·2026
Same author

General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design.

Journal of chemical information and modeling·2026
Same author

Evaluating ferroptosis susceptibility by monitoring lipid peroxidation in endoplasmic reticulum with a tailored fluorescence probe.

Biosensors & bioelectronics·2026
Same author

A Novel Motion Platform Based on Dual Driving Feet Linear Ultrasonic Motor.

Micromachines·2025
Same author

SlBES1-mediated brassinosteroid signaling suppresses flavonoid biosynthesis in tomato fruit.

Plant communications·2025
Same author

Transcriptomic analysis reveals the impact of Rutin on the proliferation, barrier function, and transcription of Porcine IPEC-J2.

Veterinary research communications·2025

Related Experiment Video

Updated: Jun 5, 2025

Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays
07:46

Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays

Published on: July 12, 2024

455

Estimation of Sow Backfat Thickness Based on Machine Vision.

Yue Jian1, Shihua Pu1, Jiaming Zhu1

  • 1Chongqing Academy of Animal Sciences, Chongqing 402460, China.

Animals : an Open Access Journal From MDPI
|December 17, 2024
PubMed
Summary

This study developed an automated machine vision system to estimate sow backfat thickness using 3D buttock imaging. The accurate, non-invasive method enhances sow management and farm economics.

Keywords:
LabVIEWbackfat thicknessbuttock morphological parametersmachine visionsows

More Related Videos

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
13:09

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Published on: April 4, 2012

16.1K
Assessment of Viability of Human Fat Injection into Nude Mice with Micro-Computed Tomography
11:13

Assessment of Viability of Human Fat Injection into Nude Mice with Micro-Computed Tomography

Published on: January 7, 2015

11.0K

Related Experiment Videos

Last Updated: Jun 5, 2025

Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays
07:46

Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays

Published on: July 12, 2024

455
Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
13:09

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Published on: April 4, 2012

16.1K
Assessment of Viability of Human Fat Injection into Nude Mice with Micro-Computed Tomography
11:13

Assessment of Viability of Human Fat Injection into Nude Mice with Micro-Computed Tomography

Published on: January 7, 2015

11.0K

Area of Science:

  • Animal Science
  • Agricultural Engineering
  • Computer Vision

Background:

  • Sow backfat thickness is crucial for reproductive performance and farm profitability.
  • Manual measurement methods are time-consuming and labor-intensive.

Purpose of the Study:

  • To develop an automated, non-invasive method for estimating sow backfat thickness.
  • To improve the efficiency and accuracy of sow management through machine vision.

Main Methods:

  • Collected 3D images and backfat data from 154 sows using Azure Kinect DK and ultrasound.
  • Extracted 10 external morphological parameters from sow buttocks.
  • Developed and validated a machine learning model for backfat thickness estimation.

Main Results:

  • A significant positive correlation (Pearson's r=0.90) was found between backfat thickness and buttock morphology.
  • The developed model achieved R²=0.8923, MAE=1.23 mm, and MAPE=5.73% on independent data.
  • The system demonstrated high accuracy meeting production requirements.

Conclusions:

  • The machine vision-based method provides an accurate and efficient alternative for measuring sow backfat.
  • This automated system can enhance sow farm management and economic benefits.
  • The research promotes the automation of swine production systems.