An in-field automatic wheat disease diagnosis system
• An in-field automatic wheat disease diagnosis system (DMIL-WDDS) is firstly proposed. • DMIL-WDDS achieves identification and localization for wheat diseases. • DMIL-WDDS outperforms conventional CNN-based architectures on recognition accuracy. • A new in-field wheat disease dataset WDD2017 is col...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lu, Jiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth - Jia, Linrui ELSEVIER, 2022, COMPAG online : an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:142 ; year:2017 ; pages:369-379 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.compag.2017.09.012 |
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ELV040710149 |
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| 520 | |a • An in-field automatic wheat disease diagnosis system (DMIL-WDDS) is firstly proposed. • DMIL-WDDS achieves identification and localization for wheat diseases. • DMIL-WDDS outperforms conventional CNN-based architectures on recognition accuracy. • A new in-field wheat disease dataset WDD2017 is collected. • DMIL-WDDS has been designed into a real-time mobile application. | ||
| 650 | 7 | |a Deep multiple instance learning |2 Elsevier | |
| 650 | 7 | |a Wheat disease detection |2 Elsevier | |
| 650 | 7 | |a Weakly supervised learning |2 Elsevier | |
| 650 | 7 | |a Fully convolutional network |2 Elsevier | |
| 650 | 7 | |a Agricultural disease diagnosis |2 Elsevier | |
| 700 | 1 | |a Hu, Jie |4 oth | |
| 700 | 1 | |a Zhao, Guannan |4 oth | |
| 700 | 1 | |a Mei, Fenghua |4 oth | |
| 700 | 1 | |a Zhang, Changshui |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Jia, Linrui ELSEVIER |t Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |d 2022 |d COMPAG online : an international journal |g Amsterdam [u.a.] |w (DE-627)ELV008658315 |
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10.1016/j.compag.2017.09.012 doi GBV00000000000255A.pica (DE-627)ELV040710149 (ELSEVIER)S0168-1699(17)30599-9 DE-627 ger DE-627 rakwb eng 620 630 640 004 620 DE-600 630 DE-600 640 DE-600 004 DE-600 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Lu, Jiang verfasserin aut An in-field automatic wheat disease diagnosis system 2017 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • An in-field automatic wheat disease diagnosis system (DMIL-WDDS) is firstly proposed. • DMIL-WDDS achieves identification and localization for wheat diseases. • DMIL-WDDS outperforms conventional CNN-based architectures on recognition accuracy. • A new in-field wheat disease dataset WDD2017 is collected. • DMIL-WDDS has been designed into a real-time mobile application. Deep multiple instance learning Elsevier Wheat disease detection Elsevier Weakly supervised learning Elsevier Fully convolutional network Elsevier Agricultural disease diagnosis Elsevier Hu, Jie oth Zhao, Guannan oth Mei, Fenghua oth Zhang, Changshui oth Enthalten in Elsevier Science Jia, Linrui ELSEVIER Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth 2022 COMPAG online : an international journal Amsterdam [u.a.] (DE-627)ELV008658315 volume:142 year:2017 pages:369-379 extent:11 https://doi.org/10.1016/j.compag.2017.09.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 142 2017 369-379 11 045F 620 |
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Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |
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eng |
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2017 |
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Lu, Jiang |
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Elektronische Aufsätze |
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Lu, Jiang |
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10.1016/j.compag.2017.09.012 |
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620 630 640 004 690 |
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an in-field automatic wheat disease diagnosis system |
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An in-field automatic wheat disease diagnosis system |
| abstract |
• An in-field automatic wheat disease diagnosis system (DMIL-WDDS) is firstly proposed. • DMIL-WDDS achieves identification and localization for wheat diseases. • DMIL-WDDS outperforms conventional CNN-based architectures on recognition accuracy. • A new in-field wheat disease dataset WDD2017 is collected. • DMIL-WDDS has been designed into a real-time mobile application. |
| abstractGer |
• An in-field automatic wheat disease diagnosis system (DMIL-WDDS) is firstly proposed. • DMIL-WDDS achieves identification and localization for wheat diseases. • DMIL-WDDS outperforms conventional CNN-based architectures on recognition accuracy. • A new in-field wheat disease dataset WDD2017 is collected. • DMIL-WDDS has been designed into a real-time mobile application. |
| abstract_unstemmed |
• An in-field automatic wheat disease diagnosis system (DMIL-WDDS) is firstly proposed. • DMIL-WDDS achieves identification and localization for wheat diseases. • DMIL-WDDS outperforms conventional CNN-based architectures on recognition accuracy. • A new in-field wheat disease dataset WDD2017 is collected. • DMIL-WDDS has been designed into a real-time mobile application. |
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| title_short |
An in-field automatic wheat disease diagnosis system |
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https://doi.org/10.1016/j.compag.2017.09.012 |
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true |
| author2 |
Hu, Jie Zhao, Guannan Mei, Fenghua Zhang, Changshui |
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Hu, Jie Zhao, Guannan Mei, Fenghua Zhang, Changshui |
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10.1016/j.compag.2017.09.012 |
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2024-07-06T18:11:19.977Z |
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