Open AccessFruit Recognition Based on Convolution Neural Network
Author(s) -
Zhan Yuhui,
Yuefen Chen,
Xiaokang Li,
Mengyao Chen,
Zhaoqian Luo
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012176
Subject(s) - artificial intelligence , convolution (computer science) , computer science , convolutional neural network , artificial neural network , automation , identification (biology) , pattern recognition (psychology) , feature (linguistics) , transfer of learning , image (mathematics) , deep learning , machine learning , engineering , mechanical engineering , linguistics , philosophy , botany , biology
Traditional fruit recognition is mainly manual, which is not conducive to automation. Deep convolution neural network (DCNN) has a strong ability of feature learning and expression. It is helpful to realize intelligence in fruit sales market if it is applied to the identification of fruits. Due to the lack of standard image databases and various types of fruits, image data sets used in this paper are obtained through taking pictures of fruits and network download. Considering the small number of samples, in addition to using common image processing technology for data expansion, transfer learning technology based on vgg16 model is also adopted for fine tuning, which can reduce the training time and alleviate over fitting. Finally, six kinds of common fruits are chosen for experiments, and the test results show that the average recognition accuracy reaches 94.16%.