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Combined Infrared Thermography and Agitated Behavior in Sows Improve Estrus Detection When Applied to Supervised

Leila Cristina Salles Moura¹, Janaina Palermo Mendes², Yann Malini Ferreira^1,3

¹Animal Science Graduate Program, Federal Rural University of Rio de Janeiro (UFRRJ), Seropédica 23897-000, RJ, Brazil.

Animals : an Open Access Journal From MDPI

|October 16, 2025

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Accurate estrus detection in sows is crucial for artificial insemination success. Combining orbital thermography with machine learning and behavioral cues can predict estrus with 87% accuracy.

Area of Science:

Animal Science
Veterinary Medicine
Machine Learning Applications

Background:

Optimizing artificial insemination (AI) success in swine relies on precise estrus identification.
Infrared thermography offers a non-invasive method to monitor physiological changes associated with estrus.

Purpose of the Study:

To develop and validate a model for predicting estrus in sows using infrared thermography and machine learning.
To evaluate the efficacy of orbital region temperature changes in estrus detection.

Main Methods:

Collected infrared thermal images of sows' ocular, ear, breast, back, vulva, and perianal areas during estrus.
Analyzed thermal images using FLIR Thermal Studio Starter software.
Applied supervised machine learning models (RF, Ctree, PLS, KNN) and evaluated performance via confusion matrix.

Keywords:

behavioral changes estrus detection infrared thermography machine learning thermal imaging

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Main Results:

Significant temperature differences were observed in the orbital region between estrus and non-estrus states.
A machine learning model combining agitated behavior and orbital temperature achieved 87% accuracy in predicting estrus.
The orbital region proved to be a key indicator for estrus detection.

Conclusions:

Integrating behavioral observations with orbital thermography and machine learning shows promise for accurate estrus detection in sows.
This approach has potential for field application to improve AI efficiency in swine production.