55 / 2025-04-14 15:10:05

Monitoring of sows’ standing reflex during estrus based on machine vision

Estrus sows; Standing reflex; Segmentation; Sequence classification

3. 智慧牧场与动物福利 > （2）智慧养殖的大数据、机器学习和物联网应用

Monitoring estrus in sows is crucial for enhancing the efficiency of production management in pig farms. Standing reflex, as a clear sign of estrus and the optimal timing indicator for artificial insemination, has lacked an efficient and reliable method for monitoring. This study developed an approach based on machine vision for the automated detection of standing heat in multiple sows within stalls. Specifically, a detection strategy was proposed based on the analysis of movement patterns by first calculating the Intersection over Union (IoU) sequence for each sow, followed by classification of standing heat versus non-standing heat sequences using Long Short-Term Memory (LSTM) network. Additionally, the lying targets were excluded from sequential classification while employing the improved YOLOv8 for segmentation. A total of 1484 images were captured for instance segmentation, and 246 IoU sequences were extracted from videos for temporal classification. Experimental results showed that misclassification rate of the improved YOLOv8 was 3% in distinguishing between lying and standing postures, indicating its efficiency in identifying these two states. Moreover, the mean average precision (mAP) of segmentation reached 98.2%, with a detection latency of 7.8 ms per image, demonstrating the model's efficient capability in extracting target contours. The LSTM model achieved an accuracy of 92.6% and exhibited great applicability in processing long video sequences. The proposed method enabled accurate monitoring of standing reflex in sows, facilitating more precise control over breeding times.