Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing...
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Autor*in:	Zhao Xue [verfasserIn] Jun Fu [verfasserIn] Qiankun Fu [verfasserIn] Xiaokang Li [verfasserIn] Zhi Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	green forage maize harvest specific energy consumption response surface methodology (RSM) artificial neural network (ANN)

Übergeordnetes Werk:	In: Agriculture - MDPI AG, 2012, 13(2023), 1890, p 1890
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:1890, p 1890

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agriculture13101890

Katalog-ID:	DOAJ093191936

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520			\|a Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.
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allfields	10.3390/agriculture13101890 doi (DE-627)DOAJ093191936 (DE-599)DOAJb4923a40a5ca415d8511084b7a1e9d60 DE-627 ger DE-627 rakwb eng S1-972 Zhao Xue verfasserin aut Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery. green forage maize harvest specific energy consumption response surface methodology (RSM) artificial neural network (ANN) Agriculture (General) Jun Fu verfasserin aut Qiankun Fu verfasserin aut Xiaokang Li verfasserin aut Zhi Chen verfasserin aut In Agriculture MDPI AG, 2012 13(2023), 1890, p 1890 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:13 year:2023 number:1890, p 1890 https://doi.org/10.3390/agriculture13101890 kostenfrei https://doaj.org/article/b4923a40a5ca415d8511084b7a1e9d60 kostenfrei https://www.mdpi.com/2077-0472/13/10/1890 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 13 2023 1890, p 1890
spelling	10.3390/agriculture13101890 doi (DE-627)DOAJ093191936 (DE-599)DOAJb4923a40a5ca415d8511084b7a1e9d60 DE-627 ger DE-627 rakwb eng S1-972 Zhao Xue verfasserin aut Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery. green forage maize harvest specific energy consumption response surface methodology (RSM) artificial neural network (ANN) Agriculture (General) Jun Fu verfasserin aut Qiankun Fu verfasserin aut Xiaokang Li verfasserin aut Zhi Chen verfasserin aut In Agriculture MDPI AG, 2012 13(2023), 1890, p 1890 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:13 year:2023 number:1890, p 1890 https://doi.org/10.3390/agriculture13101890 kostenfrei https://doaj.org/article/b4923a40a5ca415d8511084b7a1e9d60 kostenfrei https://www.mdpi.com/2077-0472/13/10/1890 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 13 2023 1890, p 1890
allfields_unstemmed	10.3390/agriculture13101890 doi (DE-627)DOAJ093191936 (DE-599)DOAJb4923a40a5ca415d8511084b7a1e9d60 DE-627 ger DE-627 rakwb eng S1-972 Zhao Xue verfasserin aut Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery. green forage maize harvest specific energy consumption response surface methodology (RSM) artificial neural network (ANN) Agriculture (General) Jun Fu verfasserin aut Qiankun Fu verfasserin aut Xiaokang Li verfasserin aut Zhi Chen verfasserin aut In Agriculture MDPI AG, 2012 13(2023), 1890, p 1890 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:13 year:2023 number:1890, p 1890 https://doi.org/10.3390/agriculture13101890 kostenfrei https://doaj.org/article/b4923a40a5ca415d8511084b7a1e9d60 kostenfrei https://www.mdpi.com/2077-0472/13/10/1890 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 13 2023 1890, p 1890
allfieldsGer	10.3390/agriculture13101890 doi (DE-627)DOAJ093191936 (DE-599)DOAJb4923a40a5ca415d8511084b7a1e9d60 DE-627 ger DE-627 rakwb eng S1-972 Zhao Xue verfasserin aut Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery. green forage maize harvest specific energy consumption response surface methodology (RSM) artificial neural network (ANN) Agriculture (General) Jun Fu verfasserin aut Qiankun Fu verfasserin aut Xiaokang Li verfasserin aut Zhi Chen verfasserin aut In Agriculture MDPI AG, 2012 13(2023), 1890, p 1890 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:13 year:2023 number:1890, p 1890 https://doi.org/10.3390/agriculture13101890 kostenfrei https://doaj.org/article/b4923a40a5ca415d8511084b7a1e9d60 kostenfrei https://www.mdpi.com/2077-0472/13/10/1890 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 13 2023 1890, p 1890
allfieldsSound	10.3390/agriculture13101890 doi (DE-627)DOAJ093191936 (DE-599)DOAJb4923a40a5ca415d8511084b7a1e9d60 DE-627 ger DE-627 rakwb eng S1-972 Zhao Xue verfasserin aut Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery. green forage maize harvest specific energy consumption response surface methodology (RSM) artificial neural network (ANN) Agriculture (General) Jun Fu verfasserin aut Qiankun Fu verfasserin aut Xiaokang Li verfasserin aut Zhi Chen verfasserin aut In Agriculture MDPI AG, 2012 13(2023), 1890, p 1890 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:13 year:2023 number:1890, p 1890 https://doi.org/10.3390/agriculture13101890 kostenfrei https://doaj.org/article/b4923a40a5ca415d8511084b7a1e9d60 kostenfrei https://www.mdpi.com/2077-0472/13/10/1890 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 13 2023 1890, p 1890
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callnumber-first	S - Agriculture
author	Zhao Xue
spellingShingle	Zhao Xue misc S1-972 misc green forage maize misc harvest misc specific energy consumption misc response surface methodology (RSM) misc artificial neural network (ANN) misc Agriculture (General) Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach
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topic_title	S1-972 Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach green forage maize harvest specific energy consumption response surface methodology (RSM) artificial neural network (ANN)
topic	misc S1-972 misc green forage maize misc harvest misc specific energy consumption misc response surface methodology (RSM) misc artificial neural network (ANN) misc Agriculture (General)
topic_unstemmed	misc S1-972 misc green forage maize misc harvest misc specific energy consumption misc response surface methodology (RSM) misc artificial neural network (ANN) misc Agriculture (General)
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title	Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach
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title_full	Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach
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author_browse	Zhao Xue Jun Fu Qiankun Fu Xiaokang Li Zhi Chen
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title_sort	modeling and optimizing the performance of green forage maize harvester header using a combined response surface methodology–artificial neural network approach
callnumber	S1-972
title_auth	Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach
abstract	Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.
abstractGer	Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.
abstract_unstemmed	Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R<sup<2</sup< values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R<sup<2</sup< of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.
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title_short	Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach
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Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

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