Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism
Cultivar identification is a basic task in oil tea (<i>Camellia oleifera</i> C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/1/1 |
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| author | Xueyan Zhu Xinwei Zhang Zhao Sun Yili Zheng Shuchai Su Fengjun Chen |
| author_facet | Xueyan Zhu Xinwei Zhang Zhao Sun Yili Zheng Shuchai Su Fengjun Chen |
| author_sort | Xueyan Zhu |
| collection | DOAJ |
| description | Cultivar identification is a basic task in oil tea (<i>Camellia oleifera</i> C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars. |
| first_indexed | 2024-04-12T22:49:09Z |
| format | Article |
| id | doaj.art-5a69e2c0d49b4cf1bf0d2b23ce6db3a7 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-04-12T22:49:09Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-5a69e2c0d49b4cf1bf0d2b23ce6db3a72022-12-22T03:13:26ZengMDPI AGForests1999-49072021-12-01131110.3390/f13010001Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention MechanismXueyan Zhu0Xinwei Zhang1Zhao Sun2Yili Zheng3Shuchai Su4Fengjun Chen5School of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaKey Laboratory of Silviculture and Conversation, Ministry of Education, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaCultivar identification is a basic task in oil tea (<i>Camellia oleifera</i> C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars.https://www.mdpi.com/1999-4907/13/1/1cultivar identificationoil teadeep learningCBAMEfficientNet-B4-CBAM |
| spellingShingle | Xueyan Zhu Xinwei Zhang Zhao Sun Yili Zheng Shuchai Su Fengjun Chen Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism Forests cultivar identification oil tea deep learning CBAM EfficientNet-B4-CBAM |
| title | Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism |
| title_full | Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism |
| title_fullStr | Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism |
| title_full_unstemmed | Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism |
| title_short | Identification of Oil Tea (<i>Camellia oleifera</i> C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism |
| title_sort | identification of oil tea i camellia oleifera i c abel cultivars using efficientnet b4 cnn model with attention mechanism |
| topic | cultivar identification oil tea deep learning CBAM EfficientNet-B4-CBAM |
| url | https://www.mdpi.com/1999-4907/13/1/1 |
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