CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading
Tobacco leaf grading plays a crucial role in ensuring the quality of tobacco production. For a very long period, the grading process is manually determined by experienced experts. In recent years, some methods have been introduced to automate the grading process by utilizing the reflection images of...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10268436/ |
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| author | Qinglin He Xiaobing Zhang Jianxin Hu Zehua Sheng Qi Li Si-Yuan Cao Hui-Liang Shen |
| author_facet | Qinglin He Xiaobing Zhang Jianxin Hu Zehua Sheng Qi Li Si-Yuan Cao Hui-Liang Shen |
| author_sort | Qinglin He |
| collection | DOAJ |
| description | Tobacco leaf grading plays a crucial role in ensuring the quality of tobacco production. For a very long period, the grading process is manually determined by experienced experts. In recent years, some methods have been introduced to automate the grading process by utilizing the reflection images of tobacco leaves. However, the high visual similarity among reflection images at different grades renders a single reflection image insufficient for achieving accurate grading. Besides, the tobacco leaves with an identical grade may have inconsistent visual appearances due to their different planting locations. It is known that an expert integrates multimodal information such as visual, tactile, and planting location cues when performing grading. Inspired by this, we propose an end-to-end Cross-modal Enhancement Network, named CMENet, for automatic tobacco leaf grading. In addition to the common reflection image, the network also adopts a transmission image to incorporate the thickness information in manual grading. CMENet comprises a difference-aware fusion module and a meta self-attention module, enabling the extraction of multimodal information from the transmission image and the planting location, respectively. Experimental results demonstrate that our CMENet achieves a high grading accuracy (80.15%) when incorporating multimodal information, surpassing the performance of existing methods that rely solely on reflection images. |
| format | Article |
| id | doaj-art-ae1e6cdfe2624aabb9a0cfeaad06d479 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ae1e6cdfe2624aabb9a0cfeaad06d4792025-08-20T02:28:15ZengIEEEIEEE Access2169-35362023-01-011110920110921210.1109/ACCESS.2023.332111110268436CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf GradingQinglin He0Xiaobing Zhang1Jianxin Hu2Zehua Sheng3https://orcid.org/0000-0002-1721-9143Qi Li4Si-Yuan Cao5https://orcid.org/0000-0001-5143-4501Hui-Liang Shen6https://orcid.org/0000-0001-8469-019XCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaChina Tobacco Zhejiang Industrial Company Ltd., Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaChina Tobacco Zhejiang Industrial Company Ltd., Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaTobacco leaf grading plays a crucial role in ensuring the quality of tobacco production. For a very long period, the grading process is manually determined by experienced experts. In recent years, some methods have been introduced to automate the grading process by utilizing the reflection images of tobacco leaves. However, the high visual similarity among reflection images at different grades renders a single reflection image insufficient for achieving accurate grading. Besides, the tobacco leaves with an identical grade may have inconsistent visual appearances due to their different planting locations. It is known that an expert integrates multimodal information such as visual, tactile, and planting location cues when performing grading. Inspired by this, we propose an end-to-end Cross-modal Enhancement Network, named CMENet, for automatic tobacco leaf grading. In addition to the common reflection image, the network also adopts a transmission image to incorporate the thickness information in manual grading. CMENet comprises a difference-aware fusion module and a meta self-attention module, enabling the extraction of multimodal information from the transmission image and the planting location, respectively. Experimental results demonstrate that our CMENet achieves a high grading accuracy (80.15%) when incorporating multimodal information, surpassing the performance of existing methods that rely solely on reflection images.https://ieeexplore.ieee.org/document/10268436/Tobacco leaf gradingimage classificationconvolutional neural networkcross-modal information fusion |
| spellingShingle | Qinglin He Xiaobing Zhang Jianxin Hu Zehua Sheng Qi Li Si-Yuan Cao Hui-Liang Shen CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading IEEE Access Tobacco leaf grading image classification convolutional neural network cross-modal information fusion |
| title | CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading |
| title_full | CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading |
| title_fullStr | CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading |
| title_full_unstemmed | CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading |
| title_short | CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading |
| title_sort | cmenet a cross modal enhancement network for tobacco leaf grading |
| topic | Tobacco leaf grading image classification convolutional neural network cross-modal information fusion |
| url | https://ieeexplore.ieee.org/document/10268436/ |
| work_keys_str_mv | AT qinglinhe cmenetacrossmodalenhancementnetworkfortobaccoleafgrading AT xiaobingzhang cmenetacrossmodalenhancementnetworkfortobaccoleafgrading AT jianxinhu cmenetacrossmodalenhancementnetworkfortobaccoleafgrading AT zehuasheng cmenetacrossmodalenhancementnetworkfortobaccoleafgrading AT qili cmenetacrossmodalenhancementnetworkfortobaccoleafgrading AT siyuancao cmenetacrossmodalenhancementnetworkfortobaccoleafgrading AT huiliangshen cmenetacrossmodalenhancementnetworkfortobaccoleafgrading |