CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel
Xinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification o...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2024/6439900 |
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| _version_ | 1832546281806888960 |
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| author | Fuqin Deng Junwei Li Lanhui Fu Chuanbo Qin Yikui Zhai Hongmin Wang Ningbo Yi Nannan Li TinLun Lam |
| author_facet | Fuqin Deng Junwei Li Lanhui Fu Chuanbo Qin Yikui Zhai Hongmin Wang Ningbo Yi Nannan Li TinLun Lam |
| author_sort | Fuqin Deng |
| collection | DOAJ |
| description | Xinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification or physical and chemical analysis, which is a tedious and costly process. This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. To analyze the features of tangerine peel in different ages and evaluate the performance of CNFA module, we conducted comparative experiments using the CNFA-integrated network on the Xinhui tangerine peel dataset. The proposed algorithm is compared with related models of the proposed structure and other attention mechanisms. The experimental results showed that the proposed algorithm had an accuracy of 97.17%, precision of 96.18%, recall of 96.09%, and F1 score of 96.13% for age recognition of the tangerine peel, providing a visual solution for the intelligent development of the tangerine peel industry. |
| format | Article |
| id | doaj-art-8f7f98c56035453db541dfd30d8821fb |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-8f7f98c56035453db541dfd30d8821fb2025-02-03T07:23:23ZengWileyJournal of Food Quality1745-45572024-01-01202410.1155/2024/6439900CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine PeelFuqin Deng0Junwei Li1Lanhui Fu2Chuanbo Qin3Yikui Zhai4Hongmin Wang5Ningbo Yi6Nannan Li7TinLun Lam8School of Electronic and Information EngineeringSchool of Electronic and Information EngineeringSchool of Electronic and Information EngineeringSchool of Electronic and Information EngineeringSchool of Electronic and Information EngineeringSchool of Electronic and Information EngineeringSchool of Textile Materials and EngineeringSchool of Computer Science and EngineeringSchool of Science and EngineeringXinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification or physical and chemical analysis, which is a tedious and costly process. This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. To analyze the features of tangerine peel in different ages and evaluate the performance of CNFA module, we conducted comparative experiments using the CNFA-integrated network on the Xinhui tangerine peel dataset. The proposed algorithm is compared with related models of the proposed structure and other attention mechanisms. The experimental results showed that the proposed algorithm had an accuracy of 97.17%, precision of 96.18%, recall of 96.09%, and F1 score of 96.13% for age recognition of the tangerine peel, providing a visual solution for the intelligent development of the tangerine peel industry.http://dx.doi.org/10.1155/2024/6439900 |
| spellingShingle | Fuqin Deng Junwei Li Lanhui Fu Chuanbo Qin Yikui Zhai Hongmin Wang Ningbo Yi Nannan Li TinLun Lam CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel Journal of Food Quality |
| title | CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel |
| title_full | CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel |
| title_fullStr | CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel |
| title_full_unstemmed | CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel |
| title_short | CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel |
| title_sort | cnfa convnext fusion attention module for age recognition of the tangerine peel |
| url | http://dx.doi.org/10.1155/2024/6439900 |
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