A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model

In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng...
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Gespeichert in:

Autor*in:	Dongming Li [verfasserIn] Mengting Zhai [verfasserIn] Xinru Piao [verfasserIn] Wei Li [verfasserIn] Lijuan Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 13(2023), 7, p 1770
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:7, p 1770

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy13071770

Katalog-ID:	DOAJ093961499

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allfields	10.3390/agronomy13071770 doi (DE-627)DOAJ093961499 (DE-599)DOAJd5c2b66eefb7421d99774cbf8ddacabb DE-627 ger DE-627 rakwb eng Dongming Li verfasserin aut A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading. deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function Agriculture S Mengting Zhai verfasserin aut Xinru Piao verfasserin aut Wei Li verfasserin aut Lijuan Zhang verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 7, p 1770 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:7, p 1770 https://doi.org/10.3390/agronomy13071770 kostenfrei https://doaj.org/article/d5c2b66eefb7421d99774cbf8ddacabb kostenfrei https://www.mdpi.com/2073-4395/13/7/1770 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 1770
spelling	10.3390/agronomy13071770 doi (DE-627)DOAJ093961499 (DE-599)DOAJd5c2b66eefb7421d99774cbf8ddacabb DE-627 ger DE-627 rakwb eng Dongming Li verfasserin aut A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading. deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function Agriculture S Mengting Zhai verfasserin aut Xinru Piao verfasserin aut Wei Li verfasserin aut Lijuan Zhang verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 7, p 1770 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:7, p 1770 https://doi.org/10.3390/agronomy13071770 kostenfrei https://doaj.org/article/d5c2b66eefb7421d99774cbf8ddacabb kostenfrei https://www.mdpi.com/2073-4395/13/7/1770 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 1770
allfields_unstemmed	10.3390/agronomy13071770 doi (DE-627)DOAJ093961499 (DE-599)DOAJd5c2b66eefb7421d99774cbf8ddacabb DE-627 ger DE-627 rakwb eng Dongming Li verfasserin aut A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading. deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function Agriculture S Mengting Zhai verfasserin aut Xinru Piao verfasserin aut Wei Li verfasserin aut Lijuan Zhang verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 7, p 1770 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:7, p 1770 https://doi.org/10.3390/agronomy13071770 kostenfrei https://doaj.org/article/d5c2b66eefb7421d99774cbf8ddacabb kostenfrei https://www.mdpi.com/2073-4395/13/7/1770 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 1770
allfieldsGer	10.3390/agronomy13071770 doi (DE-627)DOAJ093961499 (DE-599)DOAJd5c2b66eefb7421d99774cbf8ddacabb DE-627 ger DE-627 rakwb eng Dongming Li verfasserin aut A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading. deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function Agriculture S Mengting Zhai verfasserin aut Xinru Piao verfasserin aut Wei Li verfasserin aut Lijuan Zhang verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 7, p 1770 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:7, p 1770 https://doi.org/10.3390/agronomy13071770 kostenfrei https://doaj.org/article/d5c2b66eefb7421d99774cbf8ddacabb kostenfrei https://www.mdpi.com/2073-4395/13/7/1770 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 1770
allfieldsSound	10.3390/agronomy13071770 doi (DE-627)DOAJ093961499 (DE-599)DOAJd5c2b66eefb7421d99774cbf8ddacabb DE-627 ger DE-627 rakwb eng Dongming Li verfasserin aut A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading. deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function Agriculture S Mengting Zhai verfasserin aut Xinru Piao verfasserin aut Wei Li verfasserin aut Lijuan Zhang verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 7, p 1770 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:7, p 1770 https://doi.org/10.3390/agronomy13071770 kostenfrei https://doaj.org/article/d5c2b66eefb7421d99774cbf8ddacabb kostenfrei https://www.mdpi.com/2073-4395/13/7/1770 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 1770
language	English
source	In Agronomy 13(2023), 7, p 1770 volume:13 year:2023 number:7, p 1770
sourceStr	In Agronomy 13(2023), 7, p 1770 volume:13 year:2023 number:7, p 1770
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function Agriculture S
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container_title	Agronomy
authorswithroles_txt_mv	Dongming Li @@aut@@ Mengting Zhai @@aut@@ Xinru Piao @@aut@@ Wei Li @@aut@@ Lijuan Zhang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. 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author	Dongming Li
spellingShingle	Dongming Li misc deep learning misc ginseng grading misc ConvNeXt misc Channel Shuffle misc structural re-parameterization misc activation function misc Agriculture misc S A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model
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topic_title	A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model deep learning ginseng grading ConvNeXt Channel Shuffle structural re-parameterization activation function
topic	misc deep learning misc ginseng grading misc ConvNeXt misc Channel Shuffle misc structural re-parameterization misc activation function misc Agriculture misc S
topic_unstemmed	misc deep learning misc ginseng grading misc ConvNeXt misc Channel Shuffle misc structural re-parameterization misc activation function misc Agriculture misc S
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title	A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model
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title_full	A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model
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title_sort	ginseng appearance quality grading method based on an improved convnext model
title_auth	A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model
abstract	In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading.
abstractGer	In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading.
abstract_unstemmed	In order to solve the problem of the small degree of variability between the features of ginseng grading classes and the resulting need for heavy reliance on professionals, this study established a ginseng dataset containing 5116 images with three classes in different contexts and proposed a ginseng-grading model based on an improved ConvNeXt framework. Firstly, a Channel Shuffle module was embedded in the backbone network after down-sampling to fully fuse the channel features and improve the model’s grading accuracy. The model’s characterization ability enriched the feature space of the convolutional block and further improved the model’s accuracy. Finally, the original activation function, GELU, was replaced with the PreLU activation function to increase the nonlinear variability of the neural network model and improve the model’s accuracy and efficiency. The experimental results show that the method demonstrated accuracy improvements of 2.46% and 4.32%, respectively, compared with the current advanced networks, Vision Transformer and Swim Transformer. Furthermore, the accuracy, precision, recall, and specificity of ginseng classification reached values of 94.44%, 91.58%, 91.04%, and 95.82%, respectively, and the loss rate was reduced to 0.24. A comparison with expert appraisal results showed high consistency, thus verifying our model’s accuracy and reliability in ginseng quality assessment and its ability to provide technical support for intelligent ginseng quality grading.
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title_short	A Ginseng Appearance Quality Grading Method Based on an Improved ConvNeXt Model
url	https://doi.org/10.3390/agronomy13071770 https://doaj.org/article/d5c2b66eefb7421d99774cbf8ddacabb https://www.mdpi.com/2073-4395/13/7/1770 https://doaj.org/toc/2073-4395
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up_date	2024-07-03T20:25:59.387Z
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