Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification
The traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum (Chrysanthemum × morifolium Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.806711/full |
| _version_ | 1811337352365211648 |
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| author | Jue Wang Yuankai Tian Ruisong Zhang Zhilan Liu Ye Tian Silan Dai |
| author_facet | Jue Wang Yuankai Tian Ruisong Zhang Zhilan Liu Ye Tian Silan Dai |
| author_sort | Jue Wang |
| collection | DOAJ |
| description | The traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum (Chrysanthemum × morifolium Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the method of comparative morphology. However, for many cultivars, accurate recognition and classification are still a problem. Combined with the comparative morphological traits of selected samples, we proposed a multi-information model based on deep learning to recognize and classify large-flowered chrysanthemum. In this study, we collected the images of 213 large-flowered chrysanthemum cultivars in two consecutive years, 2018 and 2019. Based on the 2018 dataset, we constructed a multi-information classification model using non-pre-trained ResNet18 as the backbone network. The model achieves 70.62% top-5 test accuracy for the 2019 dataset. We explored the ability of image features to represent the characteristics of large-flowered chrysanthemum. The affinity propagation (AP) clustering shows that the features are sufficient to discriminate flower colors. The principal component analysis (PCA) shows the petal type has a better interpretation than the flower type. The training sample processing, model training scheme, and learning rate adjustment method affected the convergence and generalization of the model. The non-pre-trained model overcomes the problem of focusing on texture by ignoring colors with the ImageNet pre-trained model. These results lay a foundation for the automated recognition and classification of large-flowered chrysanthemum cultivars based on image classification. |
| first_indexed | 2024-04-13T17:53:38Z |
| format | Article |
| id | doaj.art-c76a8de344a5484eb083063eefaf75be |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-13T17:53:38Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-c76a8de344a5484eb083063eefaf75be2022-12-22T02:36:36ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-06-011310.3389/fpls.2022.806711806711Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and ClassificationJue Wang0Yuankai Tian1Ruisong Zhang2Zhilan Liu3Ye Tian4Silan Dai5Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, Beijing Laboratory of Urban and Rural Ecological Environment, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing, ChinaBeijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, Beijing Laboratory of Urban and Rural Ecological Environment, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing, ChinaCollege of Technology, Beijing Forestry University, Beijing, ChinaBeijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, Beijing Laboratory of Urban and Rural Ecological Environment, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing, ChinaCollege of Technology, Beijing Forestry University, Beijing, ChinaBeijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, Beijing Laboratory of Urban and Rural Ecological Environment, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing, ChinaThe traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum (Chrysanthemum × morifolium Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the method of comparative morphology. However, for many cultivars, accurate recognition and classification are still a problem. Combined with the comparative morphological traits of selected samples, we proposed a multi-information model based on deep learning to recognize and classify large-flowered chrysanthemum. In this study, we collected the images of 213 large-flowered chrysanthemum cultivars in two consecutive years, 2018 and 2019. Based on the 2018 dataset, we constructed a multi-information classification model using non-pre-trained ResNet18 as the backbone network. The model achieves 70.62% top-5 test accuracy for the 2019 dataset. We explored the ability of image features to represent the characteristics of large-flowered chrysanthemum. The affinity propagation (AP) clustering shows that the features are sufficient to discriminate flower colors. The principal component analysis (PCA) shows the petal type has a better interpretation than the flower type. The training sample processing, model training scheme, and learning rate adjustment method affected the convergence and generalization of the model. The non-pre-trained model overcomes the problem of focusing on texture by ignoring colors with the ImageNet pre-trained model. These results lay a foundation for the automated recognition and classification of large-flowered chrysanthemum cultivars based on image classification.https://www.frontiersin.org/articles/10.3389/fpls.2022.806711/fulllarge-flowered chrysanthemumimage classificationcultivar recognitioncultivar classificationdeep learning |
| spellingShingle | Jue Wang Yuankai Tian Ruisong Zhang Zhilan Liu Ye Tian Silan Dai Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification Frontiers in Plant Science large-flowered chrysanthemum image classification cultivar recognition cultivar classification deep learning |
| title | Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification |
| title_full | Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification |
| title_fullStr | Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification |
| title_full_unstemmed | Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification |
| title_short | Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification |
| title_sort | multi information model for large flowered chrysanthemum cultivar recognition and classification |
| topic | large-flowered chrysanthemum image classification cultivar recognition cultivar classification deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.806711/full |
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