Optical Sensing of Weed Infestations at Harvest
Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objec...
Ausführliche Beschreibung
Autor*in: |
Judit Barroso [verfasserIn] John McCallum [verfasserIn] Dan Long [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Übergeordnetes Werk: |
In: Sensors - MDPI AG, 2003, 17(2017), 10, p 2381 |
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Übergeordnetes Werk: |
volume:17 ; year:2017 ; number:10, p 2381 |
Links: |
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DOI / URN: |
10.3390/s17102381 |
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Katalog-ID: |
DOAJ078995183 |
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10.3390/s17102381 doi (DE-627)DOAJ078995183 (DE-599)DOAJ4429864d4f634ae7ae5f51efe0133abe DE-627 ger DE-627 rakwb eng TP1-1185 Judit Barroso verfasserin aut Optical Sensing of Weed Infestations at Harvest 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. weed mapping on-line optical sensing site-specific weed management Chemical technology John McCallum verfasserin aut Dan Long verfasserin aut In Sensors MDPI AG, 2003 17(2017), 10, p 2381 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:10, p 2381 https://doi.org/10.3390/s17102381 kostenfrei https://doaj.org/article/4429864d4f634ae7ae5f51efe0133abe kostenfrei https://www.mdpi.com/1424-8220/17/10/2381 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 10, p 2381 |
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10.3390/s17102381 doi (DE-627)DOAJ078995183 (DE-599)DOAJ4429864d4f634ae7ae5f51efe0133abe DE-627 ger DE-627 rakwb eng TP1-1185 Judit Barroso verfasserin aut Optical Sensing of Weed Infestations at Harvest 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. weed mapping on-line optical sensing site-specific weed management Chemical technology John McCallum verfasserin aut Dan Long verfasserin aut In Sensors MDPI AG, 2003 17(2017), 10, p 2381 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:10, p 2381 https://doi.org/10.3390/s17102381 kostenfrei https://doaj.org/article/4429864d4f634ae7ae5f51efe0133abe kostenfrei https://www.mdpi.com/1424-8220/17/10/2381 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 10, p 2381 |
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10.3390/s17102381 doi (DE-627)DOAJ078995183 (DE-599)DOAJ4429864d4f634ae7ae5f51efe0133abe DE-627 ger DE-627 rakwb eng TP1-1185 Judit Barroso verfasserin aut Optical Sensing of Weed Infestations at Harvest 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. weed mapping on-line optical sensing site-specific weed management Chemical technology John McCallum verfasserin aut Dan Long verfasserin aut In Sensors MDPI AG, 2003 17(2017), 10, p 2381 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:10, p 2381 https://doi.org/10.3390/s17102381 kostenfrei https://doaj.org/article/4429864d4f634ae7ae5f51efe0133abe kostenfrei https://www.mdpi.com/1424-8220/17/10/2381 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 10, p 2381 |
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10.3390/s17102381 doi (DE-627)DOAJ078995183 (DE-599)DOAJ4429864d4f634ae7ae5f51efe0133abe DE-627 ger DE-627 rakwb eng TP1-1185 Judit Barroso verfasserin aut Optical Sensing of Weed Infestations at Harvest 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. weed mapping on-line optical sensing site-specific weed management Chemical technology John McCallum verfasserin aut Dan Long verfasserin aut In Sensors MDPI AG, 2003 17(2017), 10, p 2381 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:10, p 2381 https://doi.org/10.3390/s17102381 kostenfrei https://doaj.org/article/4429864d4f634ae7ae5f51efe0133abe kostenfrei https://www.mdpi.com/1424-8220/17/10/2381 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 10, p 2381 |
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10.3390/s17102381 doi (DE-627)DOAJ078995183 (DE-599)DOAJ4429864d4f634ae7ae5f51efe0133abe DE-627 ger DE-627 rakwb eng TP1-1185 Judit Barroso verfasserin aut Optical Sensing of Weed Infestations at Harvest 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. weed mapping on-line optical sensing site-specific weed management Chemical technology John McCallum verfasserin aut Dan Long verfasserin aut In Sensors MDPI AG, 2003 17(2017), 10, p 2381 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:10, p 2381 https://doi.org/10.3390/s17102381 kostenfrei https://doaj.org/article/4429864d4f634ae7ae5f51efe0133abe kostenfrei https://www.mdpi.com/1424-8220/17/10/2381 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 10, p 2381 |
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Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. |
abstractGer |
Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. |
abstract_unstemmed |
Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. |
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