Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism
Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based...
Ausführliche Beschreibung
Autor*in: |
Liying Cao [verfasserIn] Hongda Li [verfasserIn] Xuerui Liu [verfasserIn] Guifen Chen [verfasserIn] Helong Yu [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2022 |
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Übergeordnetes Werk: |
In: IEEE Access - IEEE, 2014, 10(2022), Seite 76310-76317 |
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Übergeordnetes Werk: |
volume:10 ; year:2022 ; pages:76310-76317 |
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DOI / URN: |
10.1109/ACCESS.2022.3190347 |
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Katalog-ID: |
DOAJ030715911 |
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520 | |a Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem. | ||
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10.1109/ACCESS.2022.3190347 doi (DE-627)DOAJ030715911 (DE-599)DOAJ2a42b5fda0b94aa5b2b66f51ae819c42 DE-627 ger DE-627 rakwb eng TK1-9971 Liying Cao verfasserin aut Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem. Semantic segmentation attention mechanism generative adversarial network Segnet phenotypic characteristics Electrical engineering. Electronics. Nuclear engineering Hongda Li verfasserin aut Xuerui Liu verfasserin aut Guifen Chen verfasserin aut Helong Yu verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76310-76317 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76310-76317 https://doi.org/10.1109/ACCESS.2022.3190347 kostenfrei https://doaj.org/article/2a42b5fda0b94aa5b2b66f51ae819c42 kostenfrei https://ieeexplore.ieee.org/document/9828400/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76310-76317 |
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10.1109/ACCESS.2022.3190347 doi (DE-627)DOAJ030715911 (DE-599)DOAJ2a42b5fda0b94aa5b2b66f51ae819c42 DE-627 ger DE-627 rakwb eng TK1-9971 Liying Cao verfasserin aut Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem. Semantic segmentation attention mechanism generative adversarial network Segnet phenotypic characteristics Electrical engineering. Electronics. Nuclear engineering Hongda Li verfasserin aut Xuerui Liu verfasserin aut Guifen Chen verfasserin aut Helong Yu verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76310-76317 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76310-76317 https://doi.org/10.1109/ACCESS.2022.3190347 kostenfrei https://doaj.org/article/2a42b5fda0b94aa5b2b66f51ae819c42 kostenfrei https://ieeexplore.ieee.org/document/9828400/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76310-76317 |
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10.1109/ACCESS.2022.3190347 doi (DE-627)DOAJ030715911 (DE-599)DOAJ2a42b5fda0b94aa5b2b66f51ae819c42 DE-627 ger DE-627 rakwb eng TK1-9971 Liying Cao verfasserin aut Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem. Semantic segmentation attention mechanism generative adversarial network Segnet phenotypic characteristics Electrical engineering. Electronics. Nuclear engineering Hongda Li verfasserin aut Xuerui Liu verfasserin aut Guifen Chen verfasserin aut Helong Yu verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76310-76317 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76310-76317 https://doi.org/10.1109/ACCESS.2022.3190347 kostenfrei https://doaj.org/article/2a42b5fda0b94aa5b2b66f51ae819c42 kostenfrei https://ieeexplore.ieee.org/document/9828400/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76310-76317 |
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It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. 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Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism |
abstract |
Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem. |
abstractGer |
Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem. |
abstract_unstemmed |
Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem. |
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title_short |
Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism |
url |
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