Detecting sow vulva size change around estrus using machine vision technology

Xu, Ziteng; Sullivan, Riley; Zhou, Jianfeng; Bromfield, Corinne; Lim, Teng Teeh; Safranski, Timothy J.; Zheng, Yufeng

doi:10.1016/j.atech.2022.100090

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…Depth cameras have proven their potential in measuring body parts, and body weight and in detecting behavior and condition [24,108,109]. Xu et al [60] used machine vision technology to acquire a deep image of the vulva by light detection and ranging (LiDAR) camera to detect the changes in vulva size between proestrus and estrus. They concluded that vulva volume can be used as an authentic heat detection marker.…”

Section: Artificial Intelligence and Machine Learning Models In Estru...mentioning

confidence: 99%

“…In most cases, the removal of gilts is due to reproductive failure caused by a lack of observed estrus [57]. Increased replacement rate and decreased farrowing rate lead to increased non-productive days [58,59], and lower litters per sow per year (LSY) and piglets per sow per year (PSY) [60]. With a reduction in the non-productive day count from 35 to 24 days, a farm with 2400 sows can earn USD 528,000 additional revenue [61].…”

Section: Introductionmentioning

confidence: 99%

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Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

Sharifuzzaman,

Mun,

Ampode

et al. 2024

Animals

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In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

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Section: Artificial Intelligence and Machine Learning Models In Estru...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

Sharifuzzaman,

Mun,

Ampode

et al. 2024

Animals

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“…Many recent research efforts used changes in posture as an indicator of heat in sows [23]. Others attempt to detect estrus by extracting features or indicators dependent upon the change in the size around the sow's vulva [24]. This research [25] builds upon the findings of a study that highlighted the correlation between temperature changes in the vulva and the sow's reproductive status.…”

Section: Introductionmentioning

confidence: 99%

Advanced Swine Management: Infrared Imaging for Precise Localization of Reproductive Organs in Livestock Monitoring

Almadani,

Ramos,

Abuhussein

et al. 2024

Digital

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Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead to mental health issues due to repeated exposure to violence. Managing health and welfare issues becomes pivotal in group-housed animal settings, where individual care is challenging on large farms with limited staff. The necessity for computer vision systems to analyze sow behavior and detect deviations indicative of health problems is apparent. Beyond observing changes in behavior and physical traits, computer vision can accurately detect estrus based on vulva characteristics and analyze thermal imagery for temperature changes, which are crucial indicators of estrus. By automating estrus detection, farms can significantly enhance breeding efficiency, ensuring optimal timing for insemination. These systems work continuously, promptly alerting staff to anomalies for early intervention. In this research, we propose part of the solution by utilizing an image segmentation model to localize the vulva. We created our technique to identify vulvae on pig farms using infrared imagery. To accomplish this, we initially isolate the vulva region by enclosing it within a red rectangle and then generate vulva masks by applying a threshold to the red area. The system is trained using U-Net semantic segmentation, where the input for the system consists of grayscale images and their corresponding masks. We utilize U-Net semantic segmentation to find the vulva in the input image, making it lightweight, simple, and robust enough to be tested on many images. To evaluate the performance of our model, we employ the intersection over union (IOU) metric, which is a suitable indicator for determining the model’s robustness. For the segmentation model, a prediction is generally considered ‘good’ when the intersection over union score surpasses 0.5. Our model achieved this criterion with a score of 0.58, surpassing the scores of alternative methods such as the SVM with Gabor (0.515) and YOLOv3 (0.52).

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