Application Research of Non-destructive Detection of Apple Sugar Content Based on Convolution Neural Network
- Resource Type
- Conference
- Authors
- Shi, Huiji; Wang, Zaixing; Peng, Huayi; Jiang, Jiachi
- Source
- 2023 5th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT) Electronics and Communication, Network and Computer Technology (ECNCT), 2023 5th International Conference on. :168-171 Aug, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Correlation coefficient
Training
Predictive models
Feature extraction
Stability analysis
Convolutional neural networks
Root mean square
Convolutional Neural Network
Apple Sugar Content
Nondestructive Detection
Image Recognition
- Language
This paper studies the application of non-destructive testing of apple sugar content based on convolutional neural network (CNN). The Apple sample dataset was constructed and a specific network structure was designed based on VGG16, the well-trained network model was transplanted to the Android mobile phone, and the feature information related to Brix in the Apple image was successfully extracted by taking photos on the mobile phone. Experiments show that the Brix prediction method based on convolutional neural network performs well in indicators such as accuracy, recall and F1 score. Its application potential in fruit quality inspection is verified.