SCGNet: efficient sparsely connected group convolution network for wheat grains classification

IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inef...
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Autor*in:	Xuewei Sun [verfasserIn] Yan Li [verfasserIn] Guohou Li [verfasserIn] Songlin Jin [verfasserIn] Wenyi Zhao [verfasserIn] Zheng Liang [verfasserIn] Weidong Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	wheat grains classification feature multiplexing sparsely connected 3-D convolution the number of parameters

Übergeordnetes Werk:	In: Frontiers in Plant Science - Frontiers Media S.A., 2011, 14(2023)
Übergeordnetes Werk:	volume:14 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpls.2023.1304962

Katalog-ID:	DOAJ09895217X

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520			\|a IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.
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allfields	10.3389/fpls.2023.1304962 doi (DE-627)DOAJ09895217X (DE-599)DOAJ1feb2e2a88b04633b398dab3fc9664c2 DE-627 ger DE-627 rakwb eng SB1-1110 Xuewei Sun verfasserin aut SCGNet: efficient sparsely connected group convolution network for wheat grains classification 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification. wheat grains classification feature multiplexing sparsely connected 3-D convolution the number of parameters Plant culture Yan Li verfasserin aut Guohou Li verfasserin aut Songlin Jin verfasserin aut Wenyi Zhao verfasserin aut Zheng Liang verfasserin aut Weidong Zhang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1304962 kostenfrei https://doaj.org/article/1feb2e2a88b04633b398dab3fc9664c2 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1304962/full kostenfrei https://doaj.org/toc/1664-462X 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_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
spelling	10.3389/fpls.2023.1304962 doi (DE-627)DOAJ09895217X (DE-599)DOAJ1feb2e2a88b04633b398dab3fc9664c2 DE-627 ger DE-627 rakwb eng SB1-1110 Xuewei Sun verfasserin aut SCGNet: efficient sparsely connected group convolution network for wheat grains classification 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification. wheat grains classification feature multiplexing sparsely connected 3-D convolution the number of parameters Plant culture Yan Li verfasserin aut Guohou Li verfasserin aut Songlin Jin verfasserin aut Wenyi Zhao verfasserin aut Zheng Liang verfasserin aut Weidong Zhang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1304962 kostenfrei https://doaj.org/article/1feb2e2a88b04633b398dab3fc9664c2 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1304962/full kostenfrei https://doaj.org/toc/1664-462X 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_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfields_unstemmed	10.3389/fpls.2023.1304962 doi (DE-627)DOAJ09895217X (DE-599)DOAJ1feb2e2a88b04633b398dab3fc9664c2 DE-627 ger DE-627 rakwb eng SB1-1110 Xuewei Sun verfasserin aut SCGNet: efficient sparsely connected group convolution network for wheat grains classification 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification. wheat grains classification feature multiplexing sparsely connected 3-D convolution the number of parameters Plant culture Yan Li verfasserin aut Guohou Li verfasserin aut Songlin Jin verfasserin aut Wenyi Zhao verfasserin aut Zheng Liang verfasserin aut Weidong Zhang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1304962 kostenfrei https://doaj.org/article/1feb2e2a88b04633b398dab3fc9664c2 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1304962/full kostenfrei https://doaj.org/toc/1664-462X 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_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfieldsGer	10.3389/fpls.2023.1304962 doi (DE-627)DOAJ09895217X (DE-599)DOAJ1feb2e2a88b04633b398dab3fc9664c2 DE-627 ger DE-627 rakwb eng SB1-1110 Xuewei Sun verfasserin aut SCGNet: efficient sparsely connected group convolution network for wheat grains classification 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification. wheat grains classification feature multiplexing sparsely connected 3-D convolution the number of parameters Plant culture Yan Li verfasserin aut Guohou Li verfasserin aut Songlin Jin verfasserin aut Wenyi Zhao verfasserin aut Zheng Liang verfasserin aut Weidong Zhang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1304962 kostenfrei https://doaj.org/article/1feb2e2a88b04633b398dab3fc9664c2 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1304962/full kostenfrei https://doaj.org/toc/1664-462X 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_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfieldsSound	10.3389/fpls.2023.1304962 doi (DE-627)DOAJ09895217X (DE-599)DOAJ1feb2e2a88b04633b398dab3fc9664c2 DE-627 ger DE-627 rakwb eng SB1-1110 Xuewei Sun verfasserin aut SCGNet: efficient sparsely connected group convolution network for wheat grains classification 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification. wheat grains classification feature multiplexing sparsely connected 3-D convolution the number of parameters Plant culture Yan Li verfasserin aut Guohou Li verfasserin aut Songlin Jin verfasserin aut Wenyi Zhao verfasserin aut Zheng Liang verfasserin aut Weidong Zhang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1304962 kostenfrei https://doaj.org/article/1feb2e2a88b04633b398dab3fc9664c2 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1304962/full kostenfrei https://doaj.org/toc/1664-462X 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_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
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authorswithroles_txt_mv	Xuewei Sun @@aut@@ Yan Li @@aut@@ Guohou Li @@aut@@ Songlin Jin @@aut@@ Wenyi Zhao @@aut@@ Zheng Liang @@aut@@ Weidong Zhang @@aut@@
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Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wheat grains classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature multiplexing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sparsely connected</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">3-D convolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">the number of parameters</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Plant culture</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield 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callnumber-first	S - Agriculture
author	Xuewei Sun
spellingShingle	Xuewei Sun misc SB1-1110 misc wheat grains classification misc feature multiplexing misc sparsely connected misc 3-D convolution misc the number of parameters misc Plant culture SCGNet: efficient sparsely connected group convolution network for wheat grains classification
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topic_title	SB1-1110 SCGNet: efficient sparsely connected group convolution network for wheat grains classification wheat grains classification feature multiplexing sparsely connected 3-D convolution the number of parameters
topic	misc SB1-1110 misc wheat grains classification misc feature multiplexing misc sparsely connected misc 3-D convolution misc the number of parameters misc Plant culture
topic_unstemmed	misc SB1-1110 misc wheat grains classification misc feature multiplexing misc sparsely connected misc 3-D convolution misc the number of parameters misc Plant culture
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title	SCGNet: efficient sparsely connected group convolution network for wheat grains classification
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title_full	SCGNet: efficient sparsely connected group convolution network for wheat grains classification
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title_sort	scgnet: efficient sparsely connected group convolution network for wheat grains classification
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title_auth	SCGNet: efficient sparsely connected group convolution network for wheat grains classification
abstract	IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.
abstractGer	IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.
abstract_unstemmed	IntroductionEfficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.MethodsSpecifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.ResultsWe conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F1-score of 99.57%.DiscussionNotably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.
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title_short	SCGNet: efficient sparsely connected group convolution network for wheat grains classification
url	https://doi.org/10.3389/fpls.2023.1304962 https://doaj.org/article/1feb2e2a88b04633b398dab3fc9664c2 https://www.frontiersin.org/articles/10.3389/fpls.2023.1304962/full https://doaj.org/toc/1664-462X
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SCGNet: efficient sparsely connected group convolution network for wheat grains classification

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