RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis

Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel diffi...
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Autor*in:	Mahalakshmi, S. Devi [verfasserIn] Vijayalakshmi, K.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Color equalisation Face verification Linear iterative clustering Texture features SVM

Anmerkung:	© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Übergeordnetes Werk:	Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 12(2020), 7 vom: 08. Aug., Seite 7375-7389
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:7 ; day:08 ; month:08 ; pages:7375-7389

Links:	Volltext

DOI / URN:	10.1007/s12652-020-02413-0

Katalog-ID:	SPR04443863X

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520			\|a Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals.
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912			\|a GBV_ILN_213
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912			\|a GBV_ILN_293
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allfields	10.1007/s12652-020-02413-0 doi (DE-627)SPR04443863X (SPR)s12652-020-02413-0-e DE-627 ger DE-627 rakwb eng Mahalakshmi, S. Devi verfasserin aut RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. Color equalisation (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Linear iterative clustering (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 SVM (dpeaa)DE-He213 Vijayalakshmi, K. aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2020), 7 vom: 08. Aug., Seite 7375-7389 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2020 number:7 day:08 month:08 pages:7375-7389 https://dx.doi.org/10.1007/s12652-020-02413-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 7 08 08 7375-7389
spelling	10.1007/s12652-020-02413-0 doi (DE-627)SPR04443863X (SPR)s12652-020-02413-0-e DE-627 ger DE-627 rakwb eng Mahalakshmi, S. Devi verfasserin aut RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. Color equalisation (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Linear iterative clustering (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 SVM (dpeaa)DE-He213 Vijayalakshmi, K. aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2020), 7 vom: 08. Aug., Seite 7375-7389 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2020 number:7 day:08 month:08 pages:7375-7389 https://dx.doi.org/10.1007/s12652-020-02413-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 7 08 08 7375-7389
allfields_unstemmed	10.1007/s12652-020-02413-0 doi (DE-627)SPR04443863X (SPR)s12652-020-02413-0-e DE-627 ger DE-627 rakwb eng Mahalakshmi, S. Devi verfasserin aut RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. Color equalisation (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Linear iterative clustering (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 SVM (dpeaa)DE-He213 Vijayalakshmi, K. aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2020), 7 vom: 08. Aug., Seite 7375-7389 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2020 number:7 day:08 month:08 pages:7375-7389 https://dx.doi.org/10.1007/s12652-020-02413-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 7 08 08 7375-7389
allfieldsGer	10.1007/s12652-020-02413-0 doi (DE-627)SPR04443863X (SPR)s12652-020-02413-0-e DE-627 ger DE-627 rakwb eng Mahalakshmi, S. Devi verfasserin aut RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. Color equalisation (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Linear iterative clustering (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 SVM (dpeaa)DE-He213 Vijayalakshmi, K. aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2020), 7 vom: 08. Aug., Seite 7375-7389 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2020 number:7 day:08 month:08 pages:7375-7389 https://dx.doi.org/10.1007/s12652-020-02413-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 7 08 08 7375-7389
allfieldsSound	10.1007/s12652-020-02413-0 doi (DE-627)SPR04443863X (SPR)s12652-020-02413-0-e DE-627 ger DE-627 rakwb eng Mahalakshmi, S. Devi verfasserin aut RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. Color equalisation (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Linear iterative clustering (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 SVM (dpeaa)DE-He213 Vijayalakshmi, K. aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2020), 7 vom: 08. Aug., Seite 7375-7389 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2020 number:7 day:08 month:08 pages:7375-7389 https://dx.doi.org/10.1007/s12652-020-02413-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 7 08 08 7375-7389
language	English
source	Enthalten in Journal of ambient intelligence and humanized computing 12(2020), 7 vom: 08. Aug., Seite 7375-7389 volume:12 year:2020 number:7 day:08 month:08 pages:7375-7389
sourceStr	Enthalten in Journal of ambient intelligence and humanized computing 12(2020), 7 vom: 08. Aug., Seite 7375-7389 volume:12 year:2020 number:7 day:08 month:08 pages:7375-7389
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Color equalisation Face verification Linear iterative clustering Texture features SVM
isfreeaccess_bool	false
container_title	Journal of ambient intelligence and humanized computing
authorswithroles_txt_mv	Mahalakshmi, S. Devi @@aut@@ Vijayalakshmi, K. @@aut@@
publishDateDaySort_date	2020-08-08T00:00:00Z
hierarchy_top_id	620775734
id	SPR04443863X
language_de	englisch
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author	Mahalakshmi, S. Devi
spellingShingle	Mahalakshmi, S. Devi misc Color equalisation misc Face verification misc Linear iterative clustering misc Texture features misc SVM RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis
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topic_title	RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis Color equalisation (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Linear iterative clustering (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 SVM (dpeaa)DE-He213
topic	misc Color equalisation misc Face verification misc Linear iterative clustering misc Texture features misc SVM
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title	RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis
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title_full	RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis
author_sort	Mahalakshmi, S. Devi
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author_browse	Mahalakshmi, S. Devi Vijayalakshmi, K.
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doi_str_mv	10.1007/s12652-020-02413-0
title_sort	retracted article: agro suraksha: pest and disease detection for corn field using image analysis
title_auth	RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis
abstract	Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. © Springer-Verlag GmbH Germany, part of Springer Nature 2020
abstractGer	Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. © Springer-Verlag GmbH Germany, part of Springer Nature 2020
abstract_unstemmed	Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals. © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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title_short	RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis
url	https://dx.doi.org/10.1007/s12652-020-02413-0
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author2	Vijayalakshmi, K.
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up_date	2024-07-04T00:40:46.367Z
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Devi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">RETRACTED ARTICLE: Agro Suraksha: pest and disease detection for corn field using image analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. 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