Dairy Cow Behavior Recognition Technology Based on Machine Learning Classification

For the livestock industry, the health of cattle is closely related to the quality of their products and the profitability of the enterprise. Timely detection of the health condition of cattle during the farming process is essential to prevent widespread infections or diseases. This study employs inertial sensing devices combined with various machine learning techniques for the classification and comparison of cattle behavior, thus achieving cattle behavior recognition technology. Initially, accelerometer data is collected from different cattle behaviors, and features are extracted from the data. Machine learning algorithms are then applied to classify these features, resulting in the implementation of cattle behavior recognition technology. The computational analysis presents the recognition rates more than 90% for six cattle behaviors and the best case with some behaviors even reaches 95%. With accurate cattle behavior analysis technology in place, livestock operators can gain insights into the activity patterns of cattle, enabling them to identify abnormal health conditions and facilitate early treatment, thus reducing losses.