Predictive modeling of cattle calving time emphasizing abnormal and normal cases by using posture analysis

Accurate calving time prediction plays a critical role in ensuring the well-being of both mother and calf during parturition. Challenges during the calving process, particularly in abnormal cases, often necessitate human intervention to prevent potentially fatal outcomes. This study proposes a novel system for automated prediction of normal and abnormal cattle calving cases based on posture analysis. By analyzing changes in posture and identifying specific posture types exhibited by cattle, the system aims to provide early warnings of impending calving events, enabling timely intervention and risk mitigation measures. Leveraging advanced computer vision techniques, particularly the Mask R-CNN from the Detectron2 detection and the YOLOv8-pose classification method known for their efficient training time and overall accuracy, the system analyzes the frequency of posture changes and key postures like sitting, standing, feeding, sitting with extended legs, and tail-raised to predict calving cases with high precision. We discovered that the "sitting with leg extended" posture is a crucial indicator for abnormal calving events. By incorporating this posture into the classification process, the system aims to achieve high accuracy in predicting both normal and abnormal calving timeframes. Additionally, the system differentiates between normal and abnormal calving patterns by analyzing posture sequences leading up to parturition, focusing on timeframes such as 30 min, 1 h, and 2 h pre-calving. This comprehensive analysis aids in identifying potential calving complications and enables the implementation of proactive management strategies. By offering insights into optimal methods for predicting specific postures and optimizing calving time management practices, this research contributes to the field of precision livestock farming, ultimately enhancing animal welfare and reducing calving-related risks.