Classification of plug seedling quality by improved convolutional neural network with an attention mechanism
The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 mod...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.967706/full |
| _version_ | 1818507615815073792 |
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| author | Xinwu Du Xinwu Du Laiqiang Si Xin Jin Xin Jin Pengfei Li Zhihao Yun Kaihang Gao |
| author_facet | Xinwu Du Xinwu Du Laiqiang Si Xin Jin Xin Jin Pengfei Li Zhihao Yun Kaihang Gao |
| author_sort | Xinwu Du |
| collection | DOAJ |
| description | The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification. |
| first_indexed | 2024-12-10T22:20:40Z |
| format | Article |
| id | doaj.art-e3070082ab884989815e15269907999b |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-10T22:20:40Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-e3070082ab884989815e15269907999b2022-12-22T01:31:20ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-08-011310.3389/fpls.2022.967706967706Classification of plug seedling quality by improved convolutional neural network with an attention mechanismXinwu Du0Xinwu Du1Laiqiang Si2Xin Jin3Xin Jin4Pengfei Li5Zhihao Yun6Kaihang Gao7College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaScience & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaThe classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.https://www.frontiersin.org/articles/10.3389/fpls.2022.967706/fullplug seedlingsconvolutional neural networkEfficientNet-B7-CBAM modeltransfer learningquality classification |
| spellingShingle | Xinwu Du Xinwu Du Laiqiang Si Xin Jin Xin Jin Pengfei Li Zhihao Yun Kaihang Gao Classification of plug seedling quality by improved convolutional neural network with an attention mechanism Frontiers in Plant Science plug seedlings convolutional neural network EfficientNet-B7-CBAM model transfer learning quality classification |
| title | Classification of plug seedling quality by improved convolutional neural network with an attention mechanism |
| title_full | Classification of plug seedling quality by improved convolutional neural network with an attention mechanism |
| title_fullStr | Classification of plug seedling quality by improved convolutional neural network with an attention mechanism |
| title_full_unstemmed | Classification of plug seedling quality by improved convolutional neural network with an attention mechanism |
| title_short | Classification of plug seedling quality by improved convolutional neural network with an attention mechanism |
| title_sort | classification of plug seedling quality by improved convolutional neural network with an attention mechanism |
| topic | plug seedlings convolutional neural network EfficientNet-B7-CBAM model transfer learning quality classification |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.967706/full |
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