Study on chicken quality classification method based on K-means-RBF multi-source data fusion

Abstract

In order to realize the rapid and non-destructive testing of chicken quality in broiler storage and transportation, the characteristics of hyperspectral imaging technology was used for the map and spectrum combo features, to extract the spectral characteristics, which that reflect the intrinsic quality of chicken; and the image texture features, color features, which reflect the external characteristics of chicken were also extracted. The multi-source data fusion method was used to establish K-means-RBF prediction model of chicken meat quality rapid classification. The results show that the multi-source data fusion detection model established by K-means-RBF neural network has higher prediction accuracy than single feature classification model, and the precision is up to 100%. It is proved that the validity and necessity of multi - source data fusion method in chicken quality testing.