Fruit classification recognition methods based on improved deep confidence network
In order to solve the problems of low recognition accuracy in existing fruit classification recognition methods,based on the fruit classification recognition system,an improved deep confidence network for different fruit classification recognition was proposed.Different feature images were taken as input through 2-channel deep confidence network,and the output was classified using SoftMax.Compared with the conventional classification recognition methods,the proposed method could more accurately achieve the classifica-tion recognition of different fruits,and the multi-feature fusion recognition accuracy was the highest,with the recognition accuracy of 98.75%,which met the needs of fruit classification recognition.By optimizing the existing deep learning method,the performance of this method could be effectively improved.
fruit recognitionautomatic detectiondeep confidence networkmulti-feature fusionSoftMax classifier
NETL
NSTL
万方数据
