Analysis of pig activity level and body temperature variation based on ear tag data

The behavioral and physiological patterns of pigs are key indicators for assessing their health status. This study aims to explore the differences in activity level and body temperature variation patterns between normal and abnormal pigs. In view of the characteristics of pig ear tag data, such as high volatility, unclear features and strong randomness, we adopted various processing and analysis methods. The main work of this paper includes: (1) Preprocessing and integrating pig ear tag data to improve data quality; (2) Performing similarity and time series analysis on the daily data of each pig to identify the change patterns of activity level and body temperature; (3) Performing seasonal feature analysis and trend change analysis on the data of the pig population, finding the gap between abnormal pigs and the population pattern, and classifying abnormal and normal pigs. The main results and conclusions of this paper are as follows: (1) Pigs are most active in two time periods: 5:00 to 10:00 in the morning and 14:00 to 18:00 in the afternoon; (2) The activity duration of lame pigs is significantly lower than that of normal pigs, while the activity duration of pigs under other abnormal conditions is not much different; (3) Although the ear tag temperature data is affected by the pig's behavior, abnormal pigs can be found by the characteristic of low temperature trend within a day. (4) Random forest algorithm can effectively classify normal and abnormal pigs, with an accuracy of 0.879. The research results of this paper have important significance and value for realizing the refined management of pigs, improving the breeding efficiency, and ensuring the health and production safety of pigs.