Classification of bread wheat varieties with a combination of deep learning approach

Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the...
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Autor*in:	Yasar, Ali [verfasserIn] Golcuk, Adem Sari, Omer Faruk

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	BiLSTM Wheat classification CNN Xception Hybrid LSTM

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: European food research and technology - Berlin : Springer, 1999, 250(2023), 1 vom: 13. Okt., Seite 181-189
Übergeordnetes Werk:	volume:250 ; year:2023 ; number:1 ; day:13 ; month:10 ; pages:181-189

Links:	Volltext

DOI / URN:	10.1007/s00217-023-04375-x

Katalog-ID:	SPR054347688

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allfields	10.1007/s00217-023-04375-x doi (DE-627)SPR054347688 (SPR)s00217-023-04375-x-e DE-627 ger DE-627 rakwb eng Yasar, Ali verfasserin aut Classification of bread wheat varieties with a combination of deep learning approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. BiLSTM (dpeaa)DE-He213 Wheat classification (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Xception (dpeaa)DE-He213 Hybrid (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Golcuk, Adem (orcid)0000-0002-6734-5906 aut Sari, Omer Faruk aut Enthalten in European food research and technology Berlin : Springer, 1999 250(2023), 1 vom: 13. Okt., Seite 181-189 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:250 year:2023 number:1 day:13 month:10 pages:181-189 https://dx.doi.org/10.1007/s00217-023-04375-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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 250 2023 1 13 10 181-189
spelling	10.1007/s00217-023-04375-x doi (DE-627)SPR054347688 (SPR)s00217-023-04375-x-e DE-627 ger DE-627 rakwb eng Yasar, Ali verfasserin aut Classification of bread wheat varieties with a combination of deep learning approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. BiLSTM (dpeaa)DE-He213 Wheat classification (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Xception (dpeaa)DE-He213 Hybrid (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Golcuk, Adem (orcid)0000-0002-6734-5906 aut Sari, Omer Faruk aut Enthalten in European food research and technology Berlin : Springer, 1999 250(2023), 1 vom: 13. Okt., Seite 181-189 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:250 year:2023 number:1 day:13 month:10 pages:181-189 https://dx.doi.org/10.1007/s00217-023-04375-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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 250 2023 1 13 10 181-189
allfields_unstemmed	10.1007/s00217-023-04375-x doi (DE-627)SPR054347688 (SPR)s00217-023-04375-x-e DE-627 ger DE-627 rakwb eng Yasar, Ali verfasserin aut Classification of bread wheat varieties with a combination of deep learning approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. BiLSTM (dpeaa)DE-He213 Wheat classification (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Xception (dpeaa)DE-He213 Hybrid (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Golcuk, Adem (orcid)0000-0002-6734-5906 aut Sari, Omer Faruk aut Enthalten in European food research and technology Berlin : Springer, 1999 250(2023), 1 vom: 13. Okt., Seite 181-189 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:250 year:2023 number:1 day:13 month:10 pages:181-189 https://dx.doi.org/10.1007/s00217-023-04375-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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 250 2023 1 13 10 181-189
allfieldsGer	10.1007/s00217-023-04375-x doi (DE-627)SPR054347688 (SPR)s00217-023-04375-x-e DE-627 ger DE-627 rakwb eng Yasar, Ali verfasserin aut Classification of bread wheat varieties with a combination of deep learning approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. BiLSTM (dpeaa)DE-He213 Wheat classification (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Xception (dpeaa)DE-He213 Hybrid (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Golcuk, Adem (orcid)0000-0002-6734-5906 aut Sari, Omer Faruk aut Enthalten in European food research and technology Berlin : Springer, 1999 250(2023), 1 vom: 13. Okt., Seite 181-189 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:250 year:2023 number:1 day:13 month:10 pages:181-189 https://dx.doi.org/10.1007/s00217-023-04375-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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 250 2023 1 13 10 181-189
allfieldsSound	10.1007/s00217-023-04375-x doi (DE-627)SPR054347688 (SPR)s00217-023-04375-x-e DE-627 ger DE-627 rakwb eng Yasar, Ali verfasserin aut Classification of bread wheat varieties with a combination of deep learning approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. BiLSTM (dpeaa)DE-He213 Wheat classification (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Xception (dpeaa)DE-He213 Hybrid (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Golcuk, Adem (orcid)0000-0002-6734-5906 aut Sari, Omer Faruk aut Enthalten in European food research and technology Berlin : Springer, 1999 250(2023), 1 vom: 13. Okt., Seite 181-189 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:250 year:2023 number:1 day:13 month:10 pages:181-189 https://dx.doi.org/10.1007/s00217-023-04375-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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 250 2023 1 13 10 181-189
language	English
source	Enthalten in European food research and technology 250(2023), 1 vom: 13. Okt., Seite 181-189 volume:250 year:2023 number:1 day:13 month:10 pages:181-189
sourceStr	Enthalten in European food research and technology 250(2023), 1 vom: 13. Okt., Seite 181-189 volume:250 year:2023 number:1 day:13 month:10 pages:181-189
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	BiLSTM Wheat classification CNN Xception Hybrid LSTM
isfreeaccess_bool	false
container_title	European food research and technology
authorswithroles_txt_mv	Yasar, Ali @@aut@@ Golcuk, Adem @@aut@@ Sari, Omer Faruk @@aut@@
publishDateDaySort_date	2023-10-13T00:00:00Z
hierarchy_top_id	27012859X
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language_de	englisch
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author	Yasar, Ali
spellingShingle	Yasar, Ali misc BiLSTM misc Wheat classification misc CNN misc Xception misc Hybrid misc LSTM Classification of bread wheat varieties with a combination of deep learning approach
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topic_title	Classification of bread wheat varieties with a combination of deep learning approach BiLSTM (dpeaa)DE-He213 Wheat classification (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Xception (dpeaa)DE-He213 Hybrid (dpeaa)DE-He213 LSTM (dpeaa)DE-He213
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title	Classification of bread wheat varieties with a combination of deep learning approach
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title_full	Classification of bread wheat varieties with a combination of deep learning approach
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author_browse	Yasar, Ali Golcuk, Adem Sari, Omer Faruk
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title_sort	classification of bread wheat varieties with a combination of deep learning approach
title_auth	Classification of bread wheat varieties with a combination of deep learning approach
abstract	Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Classification of bread wheat varieties with a combination of deep learning approach
url	https://dx.doi.org/10.1007/s00217-023-04375-x
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author2	Golcuk, Adem Sari, Omer Faruk
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up_date	2024-07-04T01:10:35.514Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR054347688</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240112064647.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240112s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00217-023-04375-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR054347688</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00217-023-04375-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yasar, Ali</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Classification of bread wheat varieties with a combination of deep learning approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BiLSTM</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wheat classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNN</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Xception</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LSTM</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Golcuk, Adem</subfield><subfield code="0">(orcid)0000-0002-6734-5906</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sari, Omer Faruk</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">European food research and technology</subfield><subfield code="d">Berlin : Springer, 1999</subfield><subfield code="g">250(2023), 1 vom: 13. 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