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Related Concept Videos

Temperature Measurement Sites01:14

Temperature Measurement Sites

1.7K
A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
1.7K

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Related Experiment Video

Updated: Jul 10, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
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Instance Segmentation and Ensemble Learning for Automatic Temperature Detection in Multiparous Sows.

Hongxiang Xue1,2, Mingxia Shen2,3, Yuwen Sun1,2

  • 1College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

This study presents an automated method for monitoring sow core body temperature using vulva segmentation and a predictive algorithm. This approach enhances accuracy and efficiency in swine health management.

Keywords:
ensemble learninginstance segmentationsowtemperature monitoring

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Area of Science:

  • Animal Science
  • Veterinary Medicine
  • Biotechnology

Background:

  • Core body temperature is crucial for sow health monitoring.
  • Rectal thermometry is common but labor-intensive and unhygienic.
  • Current methods lack automation and precision.

Purpose of the Study:

  • To develop an automated system for sow temperature monitoring.
  • To improve accuracy and efficiency compared to traditional methods.
  • To enable autonomous temperature tracking in swine farms.

Main Methods:

  • Utilized a segmentation network combining YOLOv5s and DeepLabv3+ for vulva region identification.
  • Incorporated CBAM attention and MobileNetv2 for precise and rapid segmentation.
  • Developed a temperature prediction model using an adaptive genetic algorithm-random forest (AGA-RF) regression algorithm.

Main Results:

  • Achieved a vulvar segmentation IoU of 91.50%.
  • Predicted rectal temperature with Mean Squared Error (MSE) of 0.114 °C, Mean Absolute Error (MAE) of 0.191 °C, and R-squared (R²) of 0.845.
  • Demonstrated high reliability and practicality for autonomous monitoring.

Conclusions:

  • The proposed automated system offers a reliable and practical solution for sow temperature monitoring.
  • This method addresses the limitations of manual rectal thermometry.
  • Facilitates efficient and autonomous health management in swine production.