Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase
Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cross, Amanda J. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) - Raggi, M. ELSEVIER, 2015, San Diego, Calif |
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| Übergeordnetes Werk: |
volume:173 ; year:2018 ; pages:124-133 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.biosystemseng.2018.02.005 |
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ELV044270410 |
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| 520 | |a Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. | ||
| 520 | |a Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. | ||
| 650 | 7 | |a Pigs |2 Elsevier | |
| 650 | 7 | |a Fruit fly optimization algorithm |2 Elsevier | |
| 650 | 7 | |a Artificial neural network |2 Elsevier | |
| 650 | 7 | |a Feeding behaviour |2 Elsevier | |
| 700 | 1 | |a Rohrer, Gary A. |4 oth | |
| 700 | 1 | |a Brown-Brandl, Tami M. |4 oth | |
| 700 | 1 | |a Cassady, Joseph P. |4 oth | |
| 700 | 1 | |a Keel, Brittney N. |4 oth | |
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10.1016/j.biosystemseng.2018.02.005 doi GBV00000000000378.pica (DE-627)ELV044270410 (ELSEVIER)S1537-5110(17)30475-0 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Cross, Amanda J. verfasserin aut Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Pigs Elsevier Fruit fly optimization algorithm Elsevier Artificial neural network Elsevier Feeding behaviour Elsevier Rohrer, Gary A. oth Brown-Brandl, Tami M. oth Cassady, Joseph P. oth Keel, Brittney N. oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:173 year:2018 pages:124-133 extent:10 https://doi.org/10.1016/j.biosystemseng.2018.02.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 173 2018 124-133 10 |
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10.1016/j.biosystemseng.2018.02.005 doi GBV00000000000378.pica (DE-627)ELV044270410 (ELSEVIER)S1537-5110(17)30475-0 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Cross, Amanda J. verfasserin aut Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Pigs Elsevier Fruit fly optimization algorithm Elsevier Artificial neural network Elsevier Feeding behaviour Elsevier Rohrer, Gary A. oth Brown-Brandl, Tami M. oth Cassady, Joseph P. oth Keel, Brittney N. oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:173 year:2018 pages:124-133 extent:10 https://doi.org/10.1016/j.biosystemseng.2018.02.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 173 2018 124-133 10 |
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10.1016/j.biosystemseng.2018.02.005 doi GBV00000000000378.pica (DE-627)ELV044270410 (ELSEVIER)S1537-5110(17)30475-0 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Cross, Amanda J. verfasserin aut Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Pigs Elsevier Fruit fly optimization algorithm Elsevier Artificial neural network Elsevier Feeding behaviour Elsevier Rohrer, Gary A. oth Brown-Brandl, Tami M. oth Cassady, Joseph P. oth Keel, Brittney N. oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:173 year:2018 pages:124-133 extent:10 https://doi.org/10.1016/j.biosystemseng.2018.02.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 173 2018 124-133 10 |
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10.1016/j.biosystemseng.2018.02.005 doi GBV00000000000378.pica (DE-627)ELV044270410 (ELSEVIER)S1537-5110(17)30475-0 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Cross, Amanda J. verfasserin aut Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Pigs Elsevier Fruit fly optimization algorithm Elsevier Artificial neural network Elsevier Feeding behaviour Elsevier Rohrer, Gary A. oth Brown-Brandl, Tami M. oth Cassady, Joseph P. oth Keel, Brittney N. oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:173 year:2018 pages:124-133 extent:10 https://doi.org/10.1016/j.biosystemseng.2018.02.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 173 2018 124-133 10 |
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10.1016/j.biosystemseng.2018.02.005 doi GBV00000000000378.pica (DE-627)ELV044270410 (ELSEVIER)S1537-5110(17)30475-0 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Cross, Amanda J. verfasserin aut Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Pigs Elsevier Fruit fly optimization algorithm Elsevier Artificial neural network Elsevier Feeding behaviour Elsevier Rohrer, Gary A. oth Brown-Brandl, Tami M. oth Cassady, Joseph P. oth Keel, Brittney N. oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:173 year:2018 pages:124-133 extent:10 https://doi.org/10.1016/j.biosystemseng.2018.02.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 173 2018 124-133 10 |
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feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase |
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Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase |
| abstract |
Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. |
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Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. |
| abstract_unstemmed |
Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. |
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