THz image recognition of moldy wheat based on multi-scale context and feature pyramid
Wheat is susceptible to mold growth due to storage conditions, which subsequently affects its quality; therefore, timely and rapid identification of moldy wheat is critically important. In order to achieve high-precision recognition and class classification of wheat with different degrees of mold, a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1490384/full |
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| author | Yuying Jiang Yuying Jiang Yuying Jiang Xinyu Chen Xinyu Chen Xinyu Chen Hongyi Ge Hongyi Ge Hongyi Ge Xixi Wen Xixi Wen Xixi Wen Mengdie Jiang Mengdie Jiang Mengdie Jiang Yuan Zhang Yuan Zhang Yuan Zhang |
| author_facet | Yuying Jiang Yuying Jiang Yuying Jiang Xinyu Chen Xinyu Chen Xinyu Chen Hongyi Ge Hongyi Ge Hongyi Ge Xixi Wen Xixi Wen Xixi Wen Mengdie Jiang Mengdie Jiang Mengdie Jiang Yuan Zhang Yuan Zhang Yuan Zhang |
| author_sort | Yuying Jiang |
| collection | DOAJ |
| description | Wheat is susceptible to mold growth due to storage conditions, which subsequently affects its quality; therefore, timely and rapid identification of moldy wheat is critically important. In order to achieve high-precision recognition and class classification of wheat with different degrees of mold, a multi-scale context and feature pyramid based moldy wheat recognition network (MSCFP-Net) is proposed. Firstly, the network uses the residual network ResNeXt as the baseline network, and incorporates a multi-scale contextual feature extraction module, which is more helpful to determine the important discriminative regions in the whole image to extract more image detail features. In addition, a coordinated attention mechanism module is introduced to perform global average pooling from both directions to learn the importance of different regions in the input features in a dynamically weighted manner. Moreover, a bidirectional feature pyramid network is embedded into the baseline model, so that certain coarse-grained features and fine-grained features are retained in the processed output features at the same time to improve the network recognition accuracy. Compared with the baseline network, the four evaluation indexes of Accuracy, Precision, Recall and F1-Score of MSCFP-Net are improved by 1.08%, 1.25%, 0.53% and 0.91%, respectively. In addition, a series of comparison experiments and ablation experiments show that the classification network constructed in this paper has the best fine-grained classification performance for moldy wheat THz images. |
| format | Article |
| id | doaj-art-c5fb41d23f7442cb99e87e8009cd7506 |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-c5fb41d23f7442cb99e87e8009cd75062025-08-20T02:31:51ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-06-011610.3389/fpls.2025.14903841490384THz image recognition of moldy wheat based on multi-scale context and feature pyramidYuying Jiang0Yuying Jiang1Yuying Jiang2Xinyu Chen3Xinyu Chen4Xinyu Chen5Hongyi Ge6Hongyi Ge7Hongyi Ge8Xixi Wen9Xixi Wen10Xixi Wen11Mengdie Jiang12Mengdie Jiang13Mengdie Jiang14Yuan Zhang15Yuan Zhang16Yuan Zhang17Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou, ChinaHenan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou, ChinaSchool of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou, ChinaHenan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou, ChinaHenan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou, ChinaHenan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou, ChinaHenan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou, ChinaHenan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, ChinaWheat is susceptible to mold growth due to storage conditions, which subsequently affects its quality; therefore, timely and rapid identification of moldy wheat is critically important. In order to achieve high-precision recognition and class classification of wheat with different degrees of mold, a multi-scale context and feature pyramid based moldy wheat recognition network (MSCFP-Net) is proposed. Firstly, the network uses the residual network ResNeXt as the baseline network, and incorporates a multi-scale contextual feature extraction module, which is more helpful to determine the important discriminative regions in the whole image to extract more image detail features. In addition, a coordinated attention mechanism module is introduced to perform global average pooling from both directions to learn the importance of different regions in the input features in a dynamically weighted manner. Moreover, a bidirectional feature pyramid network is embedded into the baseline model, so that certain coarse-grained features and fine-grained features are retained in the processed output features at the same time to improve the network recognition accuracy. Compared with the baseline network, the four evaluation indexes of Accuracy, Precision, Recall and F1-Score of MSCFP-Net are improved by 1.08%, 1.25%, 0.53% and 0.91%, respectively. In addition, a series of comparison experiments and ablation experiments show that the classification network constructed in this paper has the best fine-grained classification performance for moldy wheat THz images.https://www.frontiersin.org/articles/10.3389/fpls.2025.1490384/fullterahertzidentification of moldy wheatspectral imageimage classificationdeep learning |
| spellingShingle | Yuying Jiang Yuying Jiang Yuying Jiang Xinyu Chen Xinyu Chen Xinyu Chen Hongyi Ge Hongyi Ge Hongyi Ge Xixi Wen Xixi Wen Xixi Wen Mengdie Jiang Mengdie Jiang Mengdie Jiang Yuan Zhang Yuan Zhang Yuan Zhang THz image recognition of moldy wheat based on multi-scale context and feature pyramid Frontiers in Plant Science terahertz identification of moldy wheat spectral image image classification deep learning |
| title | THz image recognition of moldy wheat based on multi-scale context and feature pyramid |
| title_full | THz image recognition of moldy wheat based on multi-scale context and feature pyramid |
| title_fullStr | THz image recognition of moldy wheat based on multi-scale context and feature pyramid |
| title_full_unstemmed | THz image recognition of moldy wheat based on multi-scale context and feature pyramid |
| title_short | THz image recognition of moldy wheat based on multi-scale context and feature pyramid |
| title_sort | thz image recognition of moldy wheat based on multi scale context and feature pyramid |
| topic | terahertz identification of moldy wheat spectral image image classification deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1490384/full |
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