In-season prediction of maize stem lodging risk using an active canopy sensor
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would b...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dong, Rui [verfasserIn] Miao, Yuxin [verfasserIn] Berry, Pete [verfasserIn] Wang, Xinbing [verfasserIn] Yuan, Fei [verfasserIn] Kusnierek, Krzysztof [verfasserIn] Baker, Chris [verfasserIn] Sterling, Mark [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: European journal of agronomy - Amsterdam [u.a.] : Elsevier Science, 1992, 151 |
|---|---|
| Übergeordnetes Werk: |
volume:151 |
| DOI / URN: |
10.1016/j.eja.2023.126956 |
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| Katalog-ID: |
ELV065309758 |
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| 520 | |a Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. | ||
| 650 | 4 | |a Precision crop management | |
| 650 | 4 | |a Planting density | |
| 650 | 4 | |a Remote sensing | |
| 650 | 4 | |a Precision nitrogen management | |
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| 700 | 1 | |a Miao, Yuxin |e verfasserin |0 (orcid)0000-0001-8419-6511 |4 aut | |
| 700 | 1 | |a Berry, Pete |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xinbing |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Fei |e verfasserin |0 (orcid)0000-0001-6979-0029 |4 aut | |
| 700 | 1 | |a Kusnierek, Krzysztof |e verfasserin |0 (orcid)0000-0002-6302-3707 |4 aut | |
| 700 | 1 | |a Baker, Chris |e verfasserin |4 aut | |
| 700 | 1 | |a Sterling, Mark |e verfasserin |4 aut | |
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10.1016/j.eja.2023.126956 doi (DE-627)ELV065309758 (ELSEVIER)S1161-0301(23)00224-1 DE-627 ger DE-627 rda eng 630 640 VZ 48.50 bkl Dong, Rui verfasserin aut In-season prediction of maize stem lodging risk using an active canopy sensor 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. Precision crop management Planting density Remote sensing Precision nitrogen management Data fusion Miao, Yuxin verfasserin (orcid)0000-0001-8419-6511 aut Berry, Pete verfasserin aut Wang, Xinbing verfasserin aut Yuan, Fei verfasserin (orcid)0000-0001-6979-0029 aut Kusnierek, Krzysztof verfasserin (orcid)0000-0002-6302-3707 aut Baker, Chris verfasserin aut Sterling, Mark verfasserin aut Enthalten in European journal of agronomy Amsterdam [u.a.] : Elsevier Science, 1992 151 Online-Ressource (DE-627)320567885 (DE-600)2016158-X (DE-576)109967216 1873-7331 nnns volume:151 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.50 Pflanzenproduktion: Allgemeines VZ AR 151 |
| spelling |
10.1016/j.eja.2023.126956 doi (DE-627)ELV065309758 (ELSEVIER)S1161-0301(23)00224-1 DE-627 ger DE-627 rda eng 630 640 VZ 48.50 bkl Dong, Rui verfasserin aut In-season prediction of maize stem lodging risk using an active canopy sensor 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. Precision crop management Planting density Remote sensing Precision nitrogen management Data fusion Miao, Yuxin verfasserin (orcid)0000-0001-8419-6511 aut Berry, Pete verfasserin aut Wang, Xinbing verfasserin aut Yuan, Fei verfasserin (orcid)0000-0001-6979-0029 aut Kusnierek, Krzysztof verfasserin (orcid)0000-0002-6302-3707 aut Baker, Chris verfasserin aut Sterling, Mark verfasserin aut Enthalten in European journal of agronomy Amsterdam [u.a.] : Elsevier Science, 1992 151 Online-Ressource (DE-627)320567885 (DE-600)2016158-X (DE-576)109967216 1873-7331 nnns volume:151 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.50 Pflanzenproduktion: Allgemeines VZ AR 151 |
| allfields_unstemmed |
10.1016/j.eja.2023.126956 doi (DE-627)ELV065309758 (ELSEVIER)S1161-0301(23)00224-1 DE-627 ger DE-627 rda eng 630 640 VZ 48.50 bkl Dong, Rui verfasserin aut In-season prediction of maize stem lodging risk using an active canopy sensor 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. Precision crop management Planting density Remote sensing Precision nitrogen management Data fusion Miao, Yuxin verfasserin (orcid)0000-0001-8419-6511 aut Berry, Pete verfasserin aut Wang, Xinbing verfasserin aut Yuan, Fei verfasserin (orcid)0000-0001-6979-0029 aut Kusnierek, Krzysztof verfasserin (orcid)0000-0002-6302-3707 aut Baker, Chris verfasserin aut Sterling, Mark verfasserin aut Enthalten in European journal of agronomy Amsterdam [u.a.] : Elsevier Science, 1992 151 Online-Ressource (DE-627)320567885 (DE-600)2016158-X (DE-576)109967216 1873-7331 nnns volume:151 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.50 Pflanzenproduktion: Allgemeines VZ AR 151 |
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10.1016/j.eja.2023.126956 doi (DE-627)ELV065309758 (ELSEVIER)S1161-0301(23)00224-1 DE-627 ger DE-627 rda eng 630 640 VZ 48.50 bkl Dong, Rui verfasserin aut In-season prediction of maize stem lodging risk using an active canopy sensor 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. Precision crop management Planting density Remote sensing Precision nitrogen management Data fusion Miao, Yuxin verfasserin (orcid)0000-0001-8419-6511 aut Berry, Pete verfasserin aut Wang, Xinbing verfasserin aut Yuan, Fei verfasserin (orcid)0000-0001-6979-0029 aut Kusnierek, Krzysztof verfasserin (orcid)0000-0002-6302-3707 aut Baker, Chris verfasserin aut Sterling, Mark verfasserin aut Enthalten in European journal of agronomy Amsterdam [u.a.] : Elsevier Science, 1992 151 Online-Ressource (DE-627)320567885 (DE-600)2016158-X (DE-576)109967216 1873-7331 nnns volume:151 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.50 Pflanzenproduktion: Allgemeines VZ AR 151 |
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10.1016/j.eja.2023.126956 doi (DE-627)ELV065309758 (ELSEVIER)S1161-0301(23)00224-1 DE-627 ger DE-627 rda eng 630 640 VZ 48.50 bkl Dong, Rui verfasserin aut In-season prediction of maize stem lodging risk using an active canopy sensor 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. Precision crop management Planting density Remote sensing Precision nitrogen management Data fusion Miao, Yuxin verfasserin (orcid)0000-0001-8419-6511 aut Berry, Pete verfasserin aut Wang, Xinbing verfasserin aut Yuan, Fei verfasserin (orcid)0000-0001-6979-0029 aut Kusnierek, Krzysztof verfasserin (orcid)0000-0002-6302-3707 aut Baker, Chris verfasserin aut Sterling, Mark verfasserin aut Enthalten in European journal of agronomy Amsterdam [u.a.] : Elsevier Science, 1992 151 Online-Ressource (DE-627)320567885 (DE-600)2016158-X (DE-576)109967216 1873-7331 nnns volume:151 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.50 Pflanzenproduktion: Allgemeines VZ AR 151 |
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Dong, Rui |
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Dong, Rui ddc 630 bkl 48.50 misc Precision crop management misc Planting density misc Remote sensing misc Precision nitrogen management misc Data fusion In-season prediction of maize stem lodging risk using an active canopy sensor |
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630 640 VZ 48.50 bkl In-season prediction of maize stem lodging risk using an active canopy sensor Precision crop management Planting density Remote sensing Precision nitrogen management Data fusion |
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In-season prediction of maize stem lodging risk using an active canopy sensor |
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in-season prediction of maize stem lodging risk using an active canopy sensor |
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In-season prediction of maize stem lodging risk using an active canopy sensor |
| abstract |
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. |
| abstractGer |
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. |
| abstract_unstemmed |
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications. |
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In-season prediction of maize stem lodging risk using an active canopy sensor |
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Miao, Yuxin Berry, Pete Wang, Xinbing Yuan, Fei Kusnierek, Krzysztof Baker, Chris Sterling, Mark |
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