1
|
Chen D, Zhang H, Lin L, Zhang Z, Zeng J, Chen L, Chen X. Auto-encoder design based on the 1D-VD-CNN model for the detection of honeysuckle from unknown origin. J Pharm Biomed Anal 2023; 234:115572. [PMID: 37478551 DOI: 10.1016/j.jpba.2023.115572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/23/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023]
Abstract
The disadvantages of the traditional one-dimensional convolution neural network (1D-CNN) model based on honeysuckle near-infrared spectral data (NIRS) include high parameter quantity, low efficiency, and inability to identify unknown categories effectively. In this paper, we propose a one-dimensional very deep convolution neural network (1D-VD-CNN) and design an auto-encoder mechanism for detecting honeysuckle from unexplored habitats. First, the 1D-VD-CNN model uses the efficient very deep (VD) structure to replace the hidden layer structure in the traditional 1D-CNN model. The model can be directly applied to analyze one-dimensional near-infrared spectral data (NIRS). Second, combining the reconstruction error of the auto-encoder, a honeysuckle identification method considering an unknown origin is designed, which can solve the problem of high confidence in convolution neural networks by using an auto-encoder and reconstruction errors of the samples to be tested. Whether the sample is an unknown variety can be determined by comparing the corrected confidence level with the preset threshold value. The results show that the accuracy of the 1D-VD-CNN training set and test set is 100%, and the loss value converges to 0.001. Compared with the traditional 1D-CNN model, the parameters and FLOPs are reduced by nearly 71% and 8%, respectively. At the same time, compared with the NIRS analysis and the PLS-DA method, the 1D-VD-CNN model has higher efficiency and better recognition performance for honeysuckle near-infrared spectral classification. Meanwhile, the accuracy rate of the auto-encoder for the category detection mechanism of honeysuckle from an unknown origin is 98%. The model can quickly and efficiently classify honeysuckle from different habitats and detect honeysuckle from unexplored habitats.
Collapse
Affiliation(s)
- Dongying Chen
- College of Electronic Information Science, Fujian Jiangxia University, Fuzhou, Fujian 350108, China; Smart Home Information Collection and Processing on Internet of Things Laboratory of Digital Fujian, Fuzhou, Fujian 350108, China
| | - Hao Zhang
- College of Electronic Information Science, Fujian Jiangxia University, Fuzhou, Fujian 350108, China; Smart Home Information Collection and Processing on Internet of Things Laboratory of Digital Fujian, Fuzhou, Fujian 350108, China.
| | - Lingyan Lin
- College of Electronic Information Science, Fujian Jiangxia University, Fuzhou, Fujian 350108, China
| | - Zilong Zhang
- College of Electronic Information Science, Fujian Jiangxia University, Fuzhou, Fujian 350108, China
| | - Jian Zeng
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Lu Chen
- Institute of Agricultural Quality Standards and Testing Technology, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Xiaogang Chen
- College of Electronic Information Science, Fujian Jiangxia University, Fuzhou, Fujian 350108, China; Smart Home Information Collection and Processing on Internet of Things Laboratory of Digital Fujian, Fuzhou, Fujian 350108, China
| |
Collapse
|