Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax

Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ping Li [verfasserIn] Rongzhi Jing [verfasserIn] Xiaoli Shi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification

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.2022.820146

Katalog-ID:	DOAJ040782727

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520			\|a Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm.
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allfields	10.3389/fpls.2022.820146 doi (DE-627)DOAJ040782727 (DE-599)DOAJ0165be5edafa49f4a02855fb77f6c12f DE-627 ger DE-627 rakwb eng SB1-1110 Ping Li verfasserin aut Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm. crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification Plant culture Rongzhi Jing verfasserin aut Xiaoli Shi 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.2022.820146 kostenfrei https://doaj.org/article/0165be5edafa49f4a02855fb77f6c12f kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.820146/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.2022.820146 doi (DE-627)DOAJ040782727 (DE-599)DOAJ0165be5edafa49f4a02855fb77f6c12f DE-627 ger DE-627 rakwb eng SB1-1110 Ping Li verfasserin aut Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm. crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification Plant culture Rongzhi Jing verfasserin aut Xiaoli Shi 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.2022.820146 kostenfrei https://doaj.org/article/0165be5edafa49f4a02855fb77f6c12f kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.820146/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.2022.820146 doi (DE-627)DOAJ040782727 (DE-599)DOAJ0165be5edafa49f4a02855fb77f6c12f DE-627 ger DE-627 rakwb eng SB1-1110 Ping Li verfasserin aut Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm. crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification Plant culture Rongzhi Jing verfasserin aut Xiaoli Shi 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.2022.820146 kostenfrei https://doaj.org/article/0165be5edafa49f4a02855fb77f6c12f kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.820146/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.2022.820146 doi (DE-627)DOAJ040782727 (DE-599)DOAJ0165be5edafa49f4a02855fb77f6c12f DE-627 ger DE-627 rakwb eng SB1-1110 Ping Li verfasserin aut Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm. crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification Plant culture Rongzhi Jing verfasserin aut Xiaoli Shi 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.2022.820146 kostenfrei https://doaj.org/article/0165be5edafa49f4a02855fb77f6c12f kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.820146/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.2022.820146 doi (DE-627)DOAJ040782727 (DE-599)DOAJ0165be5edafa49f4a02855fb77f6c12f DE-627 ger DE-627 rakwb eng SB1-1110 Ping Li verfasserin aut Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm. crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification Plant culture Rongzhi Jing verfasserin aut Xiaoli Shi 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.2022.820146 kostenfrei https://doaj.org/article/0165be5edafa49f4a02855fb77f6c12f kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.820146/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
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topic_facet	crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification Plant culture
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container_title	Frontiers in Plant Science
authorswithroles_txt_mv	Ping Li @@aut@@ Rongzhi Jing @@aut@@ Xiaoli Shi @@aut@@
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callnumber-first	S - Agriculture
author	Ping Li
spellingShingle	Ping Li misc SB1-1110 misc crop disease recognition misc modified Softmax classifier misc convolutional neural networks misc modified convolutional neural networks misc disease module identification misc Plant culture Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax
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topic_title	SB1-1110 Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax crop disease recognition modified Softmax classifier convolutional neural networks modified convolutional neural networks disease module identification
topic	misc SB1-1110 misc crop disease recognition misc modified Softmax classifier misc convolutional neural networks misc modified convolutional neural networks misc disease module identification misc Plant culture
topic_unstemmed	misc SB1-1110 misc crop disease recognition misc modified Softmax classifier misc convolutional neural networks misc modified convolutional neural networks misc disease module identification misc Plant culture
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title	Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax
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title_full	Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax
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title_sort	apple disease recognition based on convolutional neural networks with modified softmax
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title_auth	Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax
abstract	Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm.
abstractGer	Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm.
abstract_unstemmed	Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm.
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title_short	Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax
url	https://doi.org/10.3389/fpls.2022.820146 https://doaj.org/article/0165be5edafa49f4a02855fb77f6c12f https://www.frontiersin.org/articles/10.3389/fpls.2022.820146/full https://doaj.org/toc/1664-462X
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