An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage
Abstract In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomiz...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84895-y |
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| _version_ | 1841544834255421440 |
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| author | Panzhen Zhao Songfeng Wang Xianwei Hao Zhisheng Wang Jun Zou Jie Ren Yingpeng Dai |
| author_facet | Panzhen Zhao Songfeng Wang Xianwei Hao Zhisheng Wang Jun Zou Jie Ren Yingpeng Dai |
| author_sort | Panzhen Zhao |
| collection | DOAJ |
| description | Abstract In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process. Then, a multi-dimensional randomization network (MRNet) is designed to recognize tobacco baking stage. The effectiveness of MRNet lies in multi-scale hidden layer feature extraction, which can effectively enhance the expression ability of features to overcome the impact of differences between different environments on the tobacco baking stage. Finally, MRNet is used as component learner for constructing an ensemble randomization network structure to distinguish the tobacco baking stage. On the constructed tobacco baking stage dataset, EMRNet achieves 89.14% accuracy with 642.96MFLOPs. Compared with SVM, MLP, BP, ELM, CRVFL and other algorithms, EMRNet shows excellent performance in accuracy and model complexity. The proposed method explores the application of image processing technology in crop baking and drying, providing theoretical support for intelligent baking technology. |
| format | Article |
| id | doaj-art-9c5926a05fe84b269fd204070278b5d4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9c5926a05fe84b269fd204070278b5d42025-01-12T12:16:49ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-024-84895-yAn ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stagePanzhen Zhao0Songfeng Wang1Xianwei Hao2Zhisheng Wang3Jun Zou4Jie Ren5Yingpeng Dai6Tobacco Research Institute of Chinese Academy of Agricultural SciencesTobacco Research Institute of Chinese Academy of Agricultural SciencesChina Tobacco Zhejiang Industrial Co., LtdAnfu Tobacco Branch of Ji’an Tobacco CompanyAnfu Tobacco Branch of Ji’an Tobacco CompanyTobacco Research Institute of Chinese Academy of Agricultural SciencesTobacco Research Institute of Chinese Academy of Agricultural SciencesAbstract In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process. Then, a multi-dimensional randomization network (MRNet) is designed to recognize tobacco baking stage. The effectiveness of MRNet lies in multi-scale hidden layer feature extraction, which can effectively enhance the expression ability of features to overcome the impact of differences between different environments on the tobacco baking stage. Finally, MRNet is used as component learner for constructing an ensemble randomization network structure to distinguish the tobacco baking stage. On the constructed tobacco baking stage dataset, EMRNet achieves 89.14% accuracy with 642.96MFLOPs. Compared with SVM, MLP, BP, ELM, CRVFL and other algorithms, EMRNet shows excellent performance in accuracy and model complexity. The proposed method explores the application of image processing technology in crop baking and drying, providing theoretical support for intelligent baking technology.https://doi.org/10.1038/s41598-024-84895-yImage processingRandomization networkCrop bakingAgricultural intelligent platformClassification |
| spellingShingle | Panzhen Zhao Songfeng Wang Xianwei Hao Zhisheng Wang Jun Zou Jie Ren Yingpeng Dai An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage Scientific Reports Image processing Randomization network Crop baking Agricultural intelligent platform Classification |
| title | An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage |
| title_full | An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage |
| title_fullStr | An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage |
| title_full_unstemmed | An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage |
| title_short | An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage |
| title_sort | ensemble multi dimensional randomization network for intelligent recognition of tobacco baking stage |
| topic | Image processing Randomization network Crop baking Agricultural intelligent platform Classification |
| url | https://doi.org/10.1038/s41598-024-84895-y |
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