Research on cattle movement behavior analysis and model optimization based on multi-source data fusion
In this study,a deep learning model was built by integrating multi-source data to accurately analyze the movement behavior of cattle.A variety of sensors were used to record spatial coordinates and time stamps to extract motion track features.Meanwhile,dynamic features of cattle motion were extracted by deep learning models.In order to ensure high quality of behavioral data,data preprocessing and quality control are carried out,and comprehensive evaluation is carried out by confounding matrix and mean square error.This study provides a reliable method for the analysis of cattle motor behavior,and demonstrates the robustness and generalization ability of the model in the experimental results.
cattle movement behaviorbehavior pattern recognitionmulti-source data fusiondeep learningmodel optimization
NETL
NSTL
万方数据
