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The high-risk behaviour of pigs from standing, sitting to lying and grovelling is the main reason that the piglets are always crushed to death. This paper conducts research based on the field of classification and recognition of pig behaviours by using triaxial acceleration sensor, which can evaluate the maternal ability of pigs and provide data basis for selecting high quality breeding pigs. Aiming at the problem of low data variability and small data range due to the small-scale activity of the pigs, which can cause poor classification accuracy. This paper first performs moving average filter processing on the x, y, and z-axis data collected by the triaxial acceleration sensor. After feature extraction and feature selection, an optimal feature subset is proposed. Experiments show that by adopting the optimal feature subset proposed, the random forest classifier adopted can classify and evaluate four basic behaviours of pigs in daily life better. The accuracy on the test set reaches 93.8%. Compared with decision tree and BP neural network, the AUC value of random forest reaches 0.957, which has obvious performance advantages. Finally, this paper also proposed an evaluation model of maternal ability for pigs and adopted maternal ability index to evaluate the maternal ability of the pigs according to the classification results.

Automatic Pig’s Maternal Ability Evaluation System Based on Behavior Data of Sensor