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

TL;DR

Combining infrared thermography and behavioral signs in sows improves estrus detection accuracy using machine learning.

Contribution

Integrating infrared thermography with agitated behavior and machine learning improves estrus detection in sows.

Findings

The orbital region showed significant temperature differences between estrus and non-estrus states in sows.

Combining agitated behavior with orbital thermography achieved 87% accuracy in estrus detection using machine learning.

Supervised machine learning models like random forest and KNN were tested for estrus prediction.

Abstract

Estrus detection allows greater fertility success with artificial insemination. Thus, evaluating changes in body surface temperature can provide an accurate model to predict estrus occurrence. Nine LW × LD crossbred sows were studied in this pilot study, and thermographic images were collected post-weaning and in sows subjected to a hormonal induction protocol. The results show that significant temperature differences exist between pre-estrus and estrus, suggesting their potential as an indicator of changes in internal temperature during estrus, with 87% accuracy in estrus identification. They are good indicators for determining estrus in sows.

The identification of estrus at the right moment allows for a higher success of fecundity with artificial insemination. Evaluating changes in body surface temperature of sows during the estrus period using an infrared thermography camera (ITC) can provide an accurate model to predict these changes. This pilot study comprised nine crossbred Large White x Landrace sows, providing 59 data records for analysis. Observed changes in the behavior and physiological signs of the sows signaled the identification of estrus. Images of the ocular area, ear tips, breast, back, vulva, and perianal area were collected with the ITC. The images were analyzed using the FLIR Thermal Studio Starter software. Infrared mean temperatures were reported and compared using ANOVA and Tukey–Kramer tests (p < 0.05). Supervised machine learning models were tested using random forest (RF), Conditional inference trees (Ctree), Partial least squares (PLS), and K-nearest neighbors (KNN), and the method performance was measured using a confusion matrix. The orbital region showed significant differences between estrus and non-estrus states in sows. In the confusion matrix, the algorithm predicted estrus with 87% accuracy in the test set, which contained 40% of the data, when agitated behavior was combined with orbital area temperature. These findings suggest the potential for integrating behavioral and physiological observations with orbital thermography and machine learning to detect estrus in sows under field conditions accurately.

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