Estrus detection in dairy cows using advanced object tracking and behavioral analysis technologies

This study employs multi-sensor data fusion, signal analysis, and machine learning techniques to monitor and identify estrus-specific behaviors, such as frequent circling and restless standing in dairy cows, to enhance the accuracy and efficiency of estrus detection. The fast Fourier transform (FFT) was applied to time series data to identify specific frequency patterns associated with estrous behaviors. Principal component analysis (PCA) was used for dimensional reduction of behavioral data, effectively revealing complex patterns and structures within the data, allowing clear differentiation between estrous and non-estrous behaviors. The results demonstrate that the adoption of advanced technologies and algorithms significantly improves the performance of estrus behavior monitoring systems. • Integration of Technologies: Sensors, signal analysis, ML for estrus detection. • Enhanced Detection Accuracy: FFT and PCA effectively distinguish behaviors. • Innovative Analysis Method: Polar coordinates analyze rotational movements. • Superior Tracking Performance: YOLOv5s+DeepSORT achieved 93.3% accuracy.