TCSRNet: a lightweight tobacco leaf curing stage recognition network model
Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1474731/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850250365495148544 |
|---|---|
| author | Panzhen Zhao Panzhen Zhao Songfeng Wang Shijiang Duan Aihua Wang Lingfeng Meng Yichong Hu |
| author_facet | Panzhen Zhao Panzhen Zhao Songfeng Wang Shijiang Duan Aihua Wang Lingfeng Meng Yichong Hu |
| author_sort | Panzhen Zhao |
| collection | DOAJ |
| description | Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages. Secondly, the incorporation of Ghost modules significantly reduces the model’s computational complexity and parameter count through parameter sharing, enabling efficient recognition of tobacco leaf curing stages. Lastly, the design of the Multi-scale Adaptive Attention Module (MAAM) enhances the model’s perception of key visual information in images, emphasizing distinctive features such as leaf texture and color, which further improves the model’s accuracy and robustness. On the constructed tobacco leaf curing stage dataset (with color images sized 224×224 pixels), TCSRNet achieves a classification accuracy of 90.35% with 158.136 MFLOPs and 1.749M parameters. Compared to models such as ResNet34, GhostNet, ShuffleNetV2×1.5, EfficientNet-b0, MobileViT-xs, MobileNetV2, MobileNetV3-large, and MobileNetV3-small, TCSRNet demonstrates superior performance in terms of accuracy, FLOPs, and parameter count. Furthermore, when evaluated on the public V2 Plant Seedlings dataset, TCSRNet maintains an impressive accuracy of 97.15% compared to other advanced network models. This research advances the development of lightweight models for recognizing tobacco leaf curing stages, providing theoretical support for smart tobacco curing technologies and injecting new momentum into the digital transformation of the tobacco industry. |
| format | Article |
| id | doaj-art-975c8731ed1f4af0bc77b8565c8f6f3d |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-975c8731ed1f4af0bc77b8565c8f6f3d2025-08-20T01:58:12ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.14747311474731TCSRNet: a lightweight tobacco leaf curing stage recognition network modelPanzhen Zhao0Panzhen Zhao1Songfeng Wang2Shijiang Duan3Aihua Wang4Lingfeng Meng5Yichong Hu6Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, ChinaGraduate School of Chinese Academy of Agricultural Sciences, Beijing, ChinaTobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, ChinaJi’an Tobacco Company of Jiangxi Province, Ji’an, ChinaTobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, ChinaTobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, ChinaJiangxi Branch of China National Tobacco Corporation, Nanchang, ChinaDue to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages. Secondly, the incorporation of Ghost modules significantly reduces the model’s computational complexity and parameter count through parameter sharing, enabling efficient recognition of tobacco leaf curing stages. Lastly, the design of the Multi-scale Adaptive Attention Module (MAAM) enhances the model’s perception of key visual information in images, emphasizing distinctive features such as leaf texture and color, which further improves the model’s accuracy and robustness. On the constructed tobacco leaf curing stage dataset (with color images sized 224×224 pixels), TCSRNet achieves a classification accuracy of 90.35% with 158.136 MFLOPs and 1.749M parameters. Compared to models such as ResNet34, GhostNet, ShuffleNetV2×1.5, EfficientNet-b0, MobileViT-xs, MobileNetV2, MobileNetV3-large, and MobileNetV3-small, TCSRNet demonstrates superior performance in terms of accuracy, FLOPs, and parameter count. Furthermore, when evaluated on the public V2 Plant Seedlings dataset, TCSRNet maintains an impressive accuracy of 97.15% compared to other advanced network models. This research advances the development of lightweight models for recognizing tobacco leaf curing stages, providing theoretical support for smart tobacco curing technologies and injecting new momentum into the digital transformation of the tobacco industry.https://www.frontiersin.org/articles/10.3389/fpls.2024.1474731/fulltobacco leaf curing stageimage classificationlightweight network modelattention mechanismsmart agriculture |
| spellingShingle | Panzhen Zhao Panzhen Zhao Songfeng Wang Shijiang Duan Aihua Wang Lingfeng Meng Yichong Hu TCSRNet: a lightweight tobacco leaf curing stage recognition network model Frontiers in Plant Science tobacco leaf curing stage image classification lightweight network model attention mechanism smart agriculture |
| title | TCSRNet: a lightweight tobacco leaf curing stage recognition network model |
| title_full | TCSRNet: a lightweight tobacco leaf curing stage recognition network model |
| title_fullStr | TCSRNet: a lightweight tobacco leaf curing stage recognition network model |
| title_full_unstemmed | TCSRNet: a lightweight tobacco leaf curing stage recognition network model |
| title_short | TCSRNet: a lightweight tobacco leaf curing stage recognition network model |
| title_sort | tcsrnet a lightweight tobacco leaf curing stage recognition network model |
| topic | tobacco leaf curing stage image classification lightweight network model attention mechanism smart agriculture |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1474731/full |
| work_keys_str_mv | AT panzhenzhao tcsrnetalightweighttobaccoleafcuringstagerecognitionnetworkmodel AT panzhenzhao tcsrnetalightweighttobaccoleafcuringstagerecognitionnetworkmodel AT songfengwang tcsrnetalightweighttobaccoleafcuringstagerecognitionnetworkmodel AT shijiangduan tcsrnetalightweighttobaccoleafcuringstagerecognitionnetworkmodel AT aihuawang tcsrnetalightweighttobaccoleafcuringstagerecognitionnetworkmodel AT lingfengmeng tcsrnetalightweighttobaccoleafcuringstagerecognitionnetworkmodel AT yichonghu tcsrnetalightweighttobaccoleafcuringstagerecognitionnetworkmodel |