Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN

The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In t...
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Gespeichert in:

Autor*in:	Jing Nie [verfasserIn] Nianyi Wang [verfasserIn] Jingbin Li [verfasserIn] Yi Wang [verfasserIn] Kang Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	series data liquid magnetization data enhancement prediction biological effect of magnetic field

Übergeordnetes Werk:	In: Frontiers in Plant Science - Frontiers Media S.A., 2011, 13(2022)
Übergeordnetes Werk:	volume:13 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpls.2022.929140

Katalog-ID:	DOAJ027971880

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allfields	10.3389/fpls.2022.929140 doi (DE-627)DOAJ027971880 (DE-599)DOAJ8ea8cff66f6543f5b4530d15f14d2b86 DE-627 ger DE-627 rakwb eng SB1-1110 Jing Nie verfasserin aut Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs. series data liquid magnetization data enhancement prediction biological effect of magnetic field Plant culture Jing Nie verfasserin aut Nianyi Wang verfasserin aut Jingbin Li verfasserin aut Jingbin Li verfasserin aut Yi Wang verfasserin aut Kang Wang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 13(2022) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:13 year:2022 https://doi.org/10.3389/fpls.2022.929140 kostenfrei https://doaj.org/article/8ea8cff66f6543f5b4530d15f14d2b86 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.929140/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 13 2022
spelling	10.3389/fpls.2022.929140 doi (DE-627)DOAJ027971880 (DE-599)DOAJ8ea8cff66f6543f5b4530d15f14d2b86 DE-627 ger DE-627 rakwb eng SB1-1110 Jing Nie verfasserin aut Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs. series data liquid magnetization data enhancement prediction biological effect of magnetic field Plant culture Jing Nie verfasserin aut Nianyi Wang verfasserin aut Jingbin Li verfasserin aut Jingbin Li verfasserin aut Yi Wang verfasserin aut Kang Wang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 13(2022) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:13 year:2022 https://doi.org/10.3389/fpls.2022.929140 kostenfrei https://doaj.org/article/8ea8cff66f6543f5b4530d15f14d2b86 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.929140/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 13 2022
allfields_unstemmed	10.3389/fpls.2022.929140 doi (DE-627)DOAJ027971880 (DE-599)DOAJ8ea8cff66f6543f5b4530d15f14d2b86 DE-627 ger DE-627 rakwb eng SB1-1110 Jing Nie verfasserin aut Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs. series data liquid magnetization data enhancement prediction biological effect of magnetic field Plant culture Jing Nie verfasserin aut Nianyi Wang verfasserin aut Jingbin Li verfasserin aut Jingbin Li verfasserin aut Yi Wang verfasserin aut Kang Wang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 13(2022) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:13 year:2022 https://doi.org/10.3389/fpls.2022.929140 kostenfrei https://doaj.org/article/8ea8cff66f6543f5b4530d15f14d2b86 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.929140/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 13 2022
allfieldsGer	10.3389/fpls.2022.929140 doi (DE-627)DOAJ027971880 (DE-599)DOAJ8ea8cff66f6543f5b4530d15f14d2b86 DE-627 ger DE-627 rakwb eng SB1-1110 Jing Nie verfasserin aut Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs. series data liquid magnetization data enhancement prediction biological effect of magnetic field Plant culture Jing Nie verfasserin aut Nianyi Wang verfasserin aut Jingbin Li verfasserin aut Jingbin Li verfasserin aut Yi Wang verfasserin aut Kang Wang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 13(2022) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:13 year:2022 https://doi.org/10.3389/fpls.2022.929140 kostenfrei https://doaj.org/article/8ea8cff66f6543f5b4530d15f14d2b86 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.929140/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 13 2022
allfieldsSound	10.3389/fpls.2022.929140 doi (DE-627)DOAJ027971880 (DE-599)DOAJ8ea8cff66f6543f5b4530d15f14d2b86 DE-627 ger DE-627 rakwb eng SB1-1110 Jing Nie verfasserin aut Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs. series data liquid magnetization data enhancement prediction biological effect of magnetic field Plant culture Jing Nie verfasserin aut Nianyi Wang verfasserin aut Jingbin Li verfasserin aut Jingbin Li verfasserin aut Yi Wang verfasserin aut Kang Wang verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 13(2022) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:13 year:2022 https://doi.org/10.3389/fpls.2022.929140 kostenfrei https://doaj.org/article/8ea8cff66f6543f5b4530d15f14d2b86 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.929140/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 13 2022
language	English
source	In Frontiers in Plant Science 13(2022) volume:13 year:2022
sourceStr	In Frontiers in Plant Science 13(2022) volume:13 year:2022
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topic_facet	series data liquid magnetization data enhancement prediction biological effect of magnetic field Plant culture
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container_title	Frontiers in Plant Science
authorswithroles_txt_mv	Jing Nie @@aut@@ Nianyi Wang @@aut@@ Jingbin Li @@aut@@ Yi Wang @@aut@@ Kang Wang @@aut@@
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callnumber-first	S - Agriculture
author	Jing Nie
spellingShingle	Jing Nie misc SB1-1110 misc series data misc liquid magnetization misc data enhancement misc prediction misc biological effect of magnetic field misc Plant culture Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN
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topic_title	SB1-1110 Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN series data liquid magnetization data enhancement prediction biological effect of magnetic field
topic	misc SB1-1110 misc series data misc liquid magnetization misc data enhancement misc prediction misc biological effect of magnetic field misc Plant culture
topic_unstemmed	misc SB1-1110 misc series data misc liquid magnetization misc data enhancement misc prediction misc biological effect of magnetic field misc Plant culture
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title	Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN
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title_full	Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN
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author_browse	Jing Nie Nianyi Wang Jingbin Li Yi Wang Kang Wang
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title_sort	prediction of liquid magnetization series data in agriculture based on enhanced cgan
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title_auth	Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN
abstract	The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs.
abstractGer	The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs.
abstract_unstemmed	The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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
title_short	Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN
url	https://doi.org/10.3389/fpls.2022.929140 https://doaj.org/article/8ea8cff66f6543f5b4530d15f14d2b86 https://www.frontiersin.org/articles/10.3389/fpls.2022.929140/full https://doaj.org/toc/1664-462X
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