Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks
Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ji, Miaomiao [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 24(2020), 20 vom: 30. März, Seite 15327-15340 |
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| Übergeordnetes Werk: |
volume:24 ; year:2020 ; number:20 ; day:30 ; month:03 ; pages:15327-15340 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-020-04866-z |
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OLC2119518041 |
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| 520 | |a Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. | ||
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| 700 | 1 | |a Zhang, Keke |4 aut | |
| 700 | 1 | |a Wu, Qiufeng |4 aut | |
| 700 | 1 | |a Deng, Zhao |4 aut | |
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10.1007/s00500-020-04866-z doi (DE-627)OLC2119518041 (DE-He213)s00500-020-04866-z-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Ji, Miaomiao verfasserin aut Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. Multi-label Crop diseases recognition Crop diseases severity estimation Convolutional neural network Computer vision Zhang, Keke aut Wu, Qiufeng aut Deng, Zhao aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 24(2020), 20 vom: 30. März, Seite 15327-15340 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:24 year:2020 number:20 day:30 month:03 pages:15327-15340 https://doi.org/10.1007/s00500-020-04866-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 24 2020 20 30 03 15327-15340 |
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10.1007/s00500-020-04866-z doi (DE-627)OLC2119518041 (DE-He213)s00500-020-04866-z-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Ji, Miaomiao verfasserin aut Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. Multi-label Crop diseases recognition Crop diseases severity estimation Convolutional neural network Computer vision Zhang, Keke aut Wu, Qiufeng aut Deng, Zhao aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 24(2020), 20 vom: 30. März, Seite 15327-15340 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:24 year:2020 number:20 day:30 month:03 pages:15327-15340 https://doi.org/10.1007/s00500-020-04866-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 24 2020 20 30 03 15327-15340 |
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10.1007/s00500-020-04866-z doi (DE-627)OLC2119518041 (DE-He213)s00500-020-04866-z-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Ji, Miaomiao verfasserin aut Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. Multi-label Crop diseases recognition Crop diseases severity estimation Convolutional neural network Computer vision Zhang, Keke aut Wu, Qiufeng aut Deng, Zhao aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 24(2020), 20 vom: 30. März, Seite 15327-15340 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:24 year:2020 number:20 day:30 month:03 pages:15327-15340 https://doi.org/10.1007/s00500-020-04866-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 24 2020 20 30 03 15327-15340 |
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Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks |
| abstract |
Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstractGer |
Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 |
| container_issue |
20 |
| title_short |
Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks |
| url |
https://doi.org/10.1007/s00500-020-04866-z |
| remote_bool |
false |
| author2 |
Zhang, Keke Wu, Qiufeng Deng, Zhao |
| author2Str |
Zhang, Keke Wu, Qiufeng Deng, Zhao |
| ppnlink |
231970536 |
| mediatype_str_mv |
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| isOA_txt |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s00500-020-04866-z |
| up_date |
2024-07-04T01:19:40.831Z |
| _version_ |
1803609420164235264 |
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| score |
7.4010878 |