Sensor-type agnostic heat detection in dairy cows using multi-autoencoders with shared latent space

Abstract

Monitoring heat events in dairy cows is crucial for determining the heat on time, and the heat events have usually been estimated using machine learning on cow behavioral data collected from wireless activity sensors recently. However, ensuring robust performance of heat detection is difficult because of the difference in data domains (e.g., sensor types) and insufficient heat-labeled data. Therefore, this study proposes a multi-autoencoder-based heat detection in dairy cows that can represent the common representation of cow behavior across the different sensors. The proposed method can train a sensor-type agnostic heat detector using entire labeled data from the two different sensor types by aligning the latent spaces for two sensors. In addition, our approach can train the model by combining anomaly detection and weakly supervised classification to improve the performance of heat detection that can reduce the dependency on label accuracy. The results showed that the proposed approach improved cow heat detection performance by approximately 46 % than independently trained autoencoders, and the average F1-score increased by up to 0.70. The proposed method also outperformed other supervised and unsupervised learning models in heat detection using our dataset. From the results, our model effectively estimates cow behaviors by integrating sensor modalities, thereby enhancing data capabilities in low-resource settings. This study can be key for addressing the detection discrepancy in time series data based on the location of the mounted sensor, and offers the advantage of practical applications to various activity sensors currently used on farms.