MOEA/D-DE based bivariate control sequence optimization of a variable-rate fertilizer applicator

•An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was developed.•MOEA/D-DE algorithm was proposed to optimize the control sequence. To realize precise control for a bivariate control system of a variab...

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Veröffentlicht in:	Computers and electronics in agriculture Jg. 167; S. 105063
Hauptverfasser:	Zhang, Jiqin, Liu, Gang, Luo, Chengming, Hu, Hao, Huang, Jiayun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.12.2019 Elsevier BV
Schlagworte:	Algorithms applicators Bivariate analysis Coefficient of variation Differential evolution (DE) Evolutionary algorithms Fertilization fertilizers General Regression Neural Network (GRNN) General regression neural networks Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Multiple objective analysis Optimal control Optimization Precision agriculture Response time Shafts (machine elements) Variable-rate fertilization Precision agriculture General Regression Neural Network (GRNN) Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Variable-rate fertilization Differential evolution (DE)
ISSN:	0168-1699, 1872-7107
Online-Zugang:	Volltext
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Abstract	•An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was developed.•MOEA/D-DE algorithm was proposed to optimize the control sequence. To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which depends on quantifying the appropriate combination of the active feed-roll length (L) and the rotational speed of the drive shaft (N). This paper presents a novel method to optimize the control sequence (L, N) to improve fertilization accuracy and uniformity, while guaranteeing the rapidity of equipment adjustment. First, the variable-rate fertilization process model was formed using an improved General Regression Neural Network (GRNN), in which the optimum spread parameter (σ=2.0304) was calculated using a differential evolutionary (DE) algorithm. Next, a three-objective problem model was developed, and the Pareto set of the control sequence was obtained using a Multi-Objective Evolutionary Algorithm based on a Decomposition (MOEA/D) algorithm. Finally, a group of control sequences representing different target fertilization rates at the weight vector of (0.90, 0.08, 0.02) was chosen and an indoor test was conducted. Results revealed that the optimized control sequence overall outperformed the traditional method. It decreased the mean relative error (RE) from 8.239% to 5.977% and coefficient of variation (CV) from 13.512% to 13.187%, while constraining the response time to around two seconds.
AbstractList	To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which depends on quantifying the appropriate combination of the active feed-roll length (L) and the rotational speed of the drive shaft (N). This paper presents a novel method to optimize the control sequence (L, N) to improve fertilization accuracy and uniformity, while guaranteeing the rapidity of equipment adjustment. First, the variable-rate fertilization process model was formed using an improved General Regression Neural Network (GRNN), in which the optimum spread parameter (σ=2.0304) was calculated using a differential evolutionary (DE) algorithm. Next, a three-objective problem model was developed, and the Pareto set of the control sequence was obtained using a Multi-Objective Evolutionary Algorithm based on a Decomposition (MOEA/D) algorithm. Finally, a group of control sequences representing different target fertilization rates at the weight vector of (0.90, 0.08, 0.02) was chosen and an indoor test was conducted. Results revealed that the optimized control sequence overall outperformed the traditional method. It decreased the mean relative error (RE) from 8.239% to 5.977% and coefficient of variation (CV) from 13.512% to 13.187%, while constraining the response time to around two seconds. •An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was developed.•MOEA/D-DE algorithm was proposed to optimize the control sequence. To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which depends on quantifying the appropriate combination of the active feed-roll length (L) and the rotational speed of the drive shaft (N). This paper presents a novel method to optimize the control sequence (L, N) to improve fertilization accuracy and uniformity, while guaranteeing the rapidity of equipment adjustment. First, the variable-rate fertilization process model was formed using an improved General Regression Neural Network (GRNN), in which the optimum spread parameter (σ=2.0304) was calculated using a differential evolutionary (DE) algorithm. Next, a three-objective problem model was developed, and the Pareto set of the control sequence was obtained using a Multi-Objective Evolutionary Algorithm based on a Decomposition (MOEA/D) algorithm. Finally, a group of control sequences representing different target fertilization rates at the weight vector of (0.90, 0.08, 0.02) was chosen and an indoor test was conducted. Results revealed that the optimized control sequence overall outperformed the traditional method. It decreased the mean relative error (RE) from 8.239% to 5.977% and coefficient of variation (CV) from 13.512% to 13.187%, while constraining the response time to around two seconds.
ArticleNumber	105063
Author	Liu, Gang Huang, Jiayun Zhang, Jiqin Luo, Chengming Hu, Hao
Author_xml	– sequence: 1 givenname: Jiqin surname: Zhang fullname: Zhang, Jiqin email: zhjq2010jasmine@163.com organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China – sequence: 2 givenname: Gang surname: Liu fullname: Liu, Gang email: pac@cau.edu.cn organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China – sequence: 3 givenname: Chengming surname: Luo fullname: Luo, Chengming email: chmluo@mail.hzau.edu.cn organization: Department of Agricultural Engineering, College of Engineering, Huazhong Agricultural University, Wuhan, Hubei 430070, China – sequence: 4 givenname: Hao surname: Hu fullname: Hu, Hao email: 13205600570@163.com organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China – sequence: 5 givenname: Jiayun surname: Huang fullname: Huang, Jiayun email: s20183081301@cau.edu.cn organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China
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Cites_doi	10.1016/j.enconman.2016.05.061 10.1016/j.energy.2017.06.089 10.1016/j.advengsoft.2009.10.003 10.1016/j.compag.2008.07.006 10.1007/s00521-014-1788-5 10.1109/TEVC.2007.892759 10.1016/j.compag.2009.08.009 10.1109/TEVC.2008.925798 10.1007/s11269-010-9601-4 10.1016/j.powtec.2007.09.004 10.1016/j.biosystemseng.2008.03.007 10.1016/j.asoc.2018.03.028 10.1109/ACCESS.2018.2814054 10.1016/j.compag.2018.08.016 10.3390/en10122066 10.1016/j.asoc.2018.08.038 10.1016/j.asoc.2017.10.023 10.13031/2013.20086 10.1016/j.compag.2010.04.004 10.1016/j.jclepro.2017.11.107 10.1016/j.compag.2010.01.001 10.1016/j.compag.2017.12.015 10.1016/j.neucom.2016.09.027 10.1016/j.still.2006.07.016 10.1023/A:1008202821328 10.1016/j.asoc.2013.07.004 10.1016/j.energy.2016.08.090 10.1007/s11119-014-9358-5 10.1016/j.autcon.2013.10.024 10.1016/j.compag.2010.03.001 10.1016/j.biosystemseng.2008.05.007 10.1016/j.chemolab.2018.10.008 10.1016/j.asoc.2018.03.053 10.1007/s12633-017-9667-1 10.13031/2013.6429 10.1016/j.jeconom.2015.02.006 10.1016/j.compag.2019.03.011 10.1007/s11069-016-2613-5 10.1016/j.asoc.2015.01.051 10.1016/j.compag.2015.03.003 10.1016/j.asoc.2017.05.030 10.1016/j.biosystemseng.2008.09.019 10.1002/ird.197
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Keywords	Precision agriculture General Regression Neural Network (GRNN) Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Variable-rate fertilization Differential evolution (DE)
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References	Xu, Ding, Qu, Li (b0245) 2018; 68 Bendu, Deepak, Murugan (b0025) 2016; 122 Colaço, de Andrade Rosa, Molin (b0035) 2014; 15 Su, Xu, Song, Wang, Wei (b0200) 2015; 8 Jones, Lawrence, Yule (b0085) 2008; 184 Topuz (b0225) 2010; 41 Maleki, Ramon, De Baerdemaeker, Mouazen (b0140) 2008; 100 Huang, Chen, Li, Guo (b0070) 2016; 114 Huang, Lan, Thomson, Fang, Hoffmann, Lacey (b0065) 2010; 71 Panda, Raju Bahubalendruni, Biswal (b0155) 2014 Zoraghi, Shahsavar, Niaki (b0295) 2017; 58 Ehtesham (b0040) 2012; 26 Taheri-Rad, Khojastehpour, Rohani, Khoramdel, Nikkhah (b0205) 2017; 135 Alameen, Al-Gaadi, Tola (b0005) 2019; 160 Yuan, Liu, Li, Zeng, Zha (b0275) 2010; 70 Hou, Zhang, Wang, Yang, Wang, Xiu, Zhao (b0055) 2017; 4 Hu, Wen, Zeng, Huang (b0060) 2017; 221 Liu, Yuan, Liu, Li, Zhou, Gu (b0125) 2010; 2 Reyes, Esquivel, Cifuentes, Ortega (b0175) 2015; 113 Trivedi, Srinivasan, Sanyal, Ghosh (b0230) 2017; 1 Fulton, Shearer, Chabra, Higgins (b0050) 2001; 44 Tola, Kataoka, Burce, Okamoto, Hata (b0215) 2008; 101 Jafari, Hemmat, Sadeghi (b0075) 2010; 73 Panda, Bahubalendruni, Biswal (b0150) 2015; 26 Kong, Guo, Pan (b0100) 2018; 38 . Yinyan, Man, Xiaochan, Odhiambo, Weimin (b0250) 2018; 144 Zheng, Song, Chen (b0290) 2013; 13 Taki, Rohani, Soheili-Fard, Abdeshahi (b0210) 2018; 172 Fulton, Shearer, Higgins, Darr, Stombaugh (b0045) 2005; 48 Yu, Yu, Lu, Yen, Cai (b0265) 2018; 67 Xin, Ni, Li, Zhou (b0235) 2018; 6 Zhang, Zang, Li, Ma, Liu (b0280) 2018; 154 Lin, Sheng, Yan, Dai, Jiang (b0115) 2018; 11 Yip, Fan, Chiang (b0255) 2014; 38 Xu, Hu, Guo, Zhang, Lu, Cai, Xie, Goodarzi, Fu, She (b0240) 2018; 183 Chen, Lu, Wang, Sun, Zhang (b0030) 2016; 02 Sayah (b0185) 2018; 73 Anantachar, Kumar, Guruswamy (b0010) 2010; 72 Back, Yu, Kim, Chung, Lee (b0015) 2014; 57 Tolis, P., 2017. Precision agriculture: future of the CAP? [WWW Document]. URL Zhang, Li (b0170) 2007; 11 Liu, Yu (b0120) 2010 Maleki, Mouazen, Ramon, De Baerdemaeker (b0135) 2008; 94 Li, Zhang (b0110) 2009; 13 Majumder, Maity (b0130) 2018; 10 Pérez-González, Begovich-Mendoza, Ruiz-León (b0160) 2018; 62 Kim, Kim, Ryu, Rhee (b0095) 2008; 100 Balafoutis, Beck, Fountas, Vangeyte, Van Der Wal, Soto, Gómez-Barbero, Barnes, Eory (b0020) 2017; 9 Zhang, Yang (b0285) 2015; 187 Niu, Wang, Chen, Liang (b0145) 2017; 10 Lanza-Gutierrez, Gomez-Pulido (b0105) 2015; 30 Storn, Price (b0195) 1997; 23 Srinivasa Raju, Nagesh Kumar (b0190) 1999; 54 Jia, Feng, Qi, Liu, Liu, Yang, Li (b0080) 2014 Kilic, Anac (b0090) 2010; 24 Prasanna Kumar, Srivastava, Nagesh (b0165) 2009; 65 Yu (b0260) 2017; 85 Yuan, Liu, Gu, Miao (b0270) 2011 Zhang (10.1016/j.compag.2019.105063_b0280) 2018; 154 Fulton (10.1016/j.compag.2019.105063_b0045) 2005; 48 Srinivasa Raju (10.1016/j.compag.2019.105063_b0190) 1999; 54 Balafoutis (10.1016/j.compag.2019.105063_b0020) 2017; 9 Zhang (10.1016/j.compag.2019.105063_b0285) 2015; 187 Trivedi (10.1016/j.compag.2019.105063_b0230) 2017; 1 Liu (10.1016/j.compag.2019.105063_b0125) 2010; 2 Maleki (10.1016/j.compag.2019.105063_b0135) 2008; 94 Anantachar (10.1016/j.compag.2019.105063_b0010) 2010; 72 Colaço (10.1016/j.compag.2019.105063_b0035) 2014; 15 Tola (10.1016/j.compag.2019.105063_b0215) 2008; 101 Prasanna Kumar (10.1016/j.compag.2019.105063_b0165) 2009; 65 Yu (10.1016/j.compag.2019.105063_b0265) 2018; 67 Fulton (10.1016/j.compag.2019.105063_b0050) 2001; 44 Zhang (10.1016/j.compag.2019.105063_b0170) 2007; 11 Xin (10.1016/j.compag.2019.105063_b0235) 2018; 6 Yuan (10.1016/j.compag.2019.105063_b0270) 2011 Bendu (10.1016/j.compag.2019.105063_b0025) 2016; 122 Maleki (10.1016/j.compag.2019.105063_b0140) 2008; 100 Panda (10.1016/j.compag.2019.105063_b0155) 2014 Xu (10.1016/j.compag.2019.105063_b0245) 2018; 68 Sayah (10.1016/j.compag.2019.105063_b0185) 2018; 73 Taheri-Rad (10.1016/j.compag.2019.105063_b0205) 2017; 135 Zheng (10.1016/j.compag.2019.105063_b0290) 2013; 13 Li (10.1016/j.compag.2019.105063_b0110) 2009; 13 Liu (10.1016/j.compag.2019.105063_b0120) 2010 Huang (10.1016/j.compag.2019.105063_b0070) 2016; 114 Taki (10.1016/j.compag.2019.105063_b0210) 2018; 172 Lin (10.1016/j.compag.2019.105063_b0115) 2018; 11 Hu (10.1016/j.compag.2019.105063_b0060) 2017; 221 Back (10.1016/j.compag.2019.105063_b0015) 2014; 57 Kilic (10.1016/j.compag.2019.105063_b0090) 2010; 24 Niu (10.1016/j.compag.2019.105063_b0145) 2017; 10 Jia (10.1016/j.compag.2019.105063_b0080) 2014 Yuan (10.1016/j.compag.2019.105063_b0275) 2010; 70 Jafari (10.1016/j.compag.2019.105063_b0075) 2010; 73 Kim (10.1016/j.compag.2019.105063_b0095) 2008; 100 Panda (10.1016/j.compag.2019.105063_b0150) 2015; 26 Chen (10.1016/j.compag.2019.105063_b0030) 2016; 02 Majumder (10.1016/j.compag.2019.105063_b0130) 2018; 10 Kong (10.1016/j.compag.2019.105063_b0100) 2018; 38 Jones (10.1016/j.compag.2019.105063_b0085) 2008; 184 Zoraghi (10.1016/j.compag.2019.105063_b0295) 2017; 58 Hou (10.1016/j.compag.2019.105063_b0055) 2017; 4 Pérez-González (10.1016/j.compag.2019.105063_b0160) 2018; 62 Xu (10.1016/j.compag.2019.105063_b0240) 2018; 183 Storn (10.1016/j.compag.2019.105063_b0195) 1997; 23 Topuz (10.1016/j.compag.2019.105063_b0225) 2010; 41 Reyes (10.1016/j.compag.2019.105063_b0175) 2015; 113 Yip (10.1016/j.compag.2019.105063_b0255) 2014; 38 Alameen (10.1016/j.compag.2019.105063_b0005) 2019; 160 Yu (10.1016/j.compag.2019.105063_b0260) 2017; 85 Ehtesham (10.1016/j.compag.2019.105063_b0040) 2012; 26 Yinyan (10.1016/j.compag.2019.105063_b0250) 2018; 144 Huang (10.1016/j.compag.2019.105063_b0065) 2010; 71 10.1016/j.compag.2019.105063_b0220 Su (10.1016/j.compag.2019.105063_b0200) 2015; 8 Lanza-Gutierrez (10.1016/j.compag.2019.105063_b0105) 2015; 30
References_xml	– volume: 26 year: 2012 ident: b0040 article-title: Design, development and field evaluation of a map-based variable rate granular fertilizer application control system publication-title: Contact Dermatitis – volume: 113 start-page: 260 year: 2015 end-page: 265 ident: b0175 article-title: Field testing of an automatic control system for variable rate fertilizer application publication-title: Comput. Electron. Agric. – volume: 85 start-page: 959 year: 2017 end-page: 976 ident: b0260 article-title: Disaster prediction model based on support vector machine for regression and improved differential evolution publication-title: Nat. Hazards – volume: 100 start-page: 498 year: 2008 end-page: 510 ident: b0095 article-title: Fertiliser application performance of a variable-rate pneumatic granular applicator for rice production publication-title: Biosyst. Eng. – volume: 72 start-page: 87 year: 2010 end-page: 98 ident: b0010 article-title: Neural network prediction of performance parameters of an inclined plate seed metering device and its reverse mapping for the determination of optimum design and operational parameters publication-title: Comput. Electron. Agric. – volume: 15 start-page: 304 year: 2014 end-page: 320 ident: b0035 article-title: A model to analyze as-applied reports from variable rate applications publication-title: Precis. Agric. – start-page: 381 year: 2014 end-page: 391 ident: b0080 article-title: Research and application of variable rate fertilizer applicator system based on a DC Motor publication-title: 7th International Conference on Computer and Computing Technologies in Agriculture – volume: 172 start-page: 3028 year: 2018 end-page: 3041 ident: b0210 article-title: Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models publication-title: J. Clean. Prod. – reference: . URL – volume: 160 start-page: 31 year: 2019 end-page: 39 ident: b0005 article-title: Development and performance evaluation of a control system for variable rate granular fertilizer application publication-title: Comput. Electron. Agric. – volume: 122 start-page: 165 year: 2016 end-page: 173 ident: b0025 article-title: Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol publication-title: Energy Convers. Manag. – volume: 26 start-page: 1129 year: 2015 end-page: 1136 ident: b0150 article-title: A general regression neural network approach for the evaluation of compressive strength of FDM prototypes publication-title: Neural Comput. Appl. – volume: 101 start-page: 411 year: 2008 end-page: 416 ident: b0215 article-title: Granular fertiliser application rate control system with integrated output volume measurement publication-title: Biosyst. Eng. – volume: 1 start-page: 93 year: 2017 end-page: 100 ident: b0230 article-title: A survey of multiobjective evolutionary algorithms based on decomposition publication-title: Proc. – 2017 IEEE Int. Conf. Comput. Sci. Eng. IEEE/IFIP Int. Conf. Embed. Ubiquitous Comput. CSE EUC 2017 – year: 2011 ident: b0270 article-title: Bivariate fertilization control sequence optimization based on relevance vector machine publication-title: J. Agric. Mach. – volume: 2 year: 2010 ident: b0125 article-title: ARM and DSP-based bivariable fertilizing control system design and implementation publication-title: J. Agric. Mach. – volume: 73 start-page: 56 year: 2010 end-page: 65 ident: b0075 article-title: Development and performance assessment of a DC electric variable-rate controller for use on grain drills publication-title: Comput. Electron. Agric. – volume: 54 start-page: 455 year: 1999 end-page: 465 ident: b0190 article-title: Multicriterion decision making in irrigation planning publication-title: Irrig. Drain. – volume: 38 start-page: 30 year: 2014 end-page: 38 ident: b0255 article-title: Predicting the maintenance cost of construction equipment: comparison between general regression neural network and Box-Jenkins time series models publication-title: Autom. Constr. – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b0170 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. – start-page: 450 year: 2010 end-page: 453 ident: b0120 article-title: A modified differential evolution algorithm and its application in thermal process model identification publication-title: Proc. 2010 IEEE Int. Conf. Intell. Syst. Knowl. Eng. ISKE 2010 – volume: 6 start-page: 19278 year: 2018 end-page: 19286 ident: b0235 article-title: General regression neural network and artificial-bee-colony based general regression neural network approaches to the number of end-of-life vehicles in China publication-title: IEEE Access – volume: 114 start-page: 1164 year: 2016 end-page: 1175 ident: b0070 article-title: Modeling of chemical exergy of agricultural biomass using improved general regression neural network publication-title: Energy – volume: 23 start-page: 341 year: 1997 end-page: 359 ident: b0195 article-title: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces publication-title: J. Glob. Optim. – volume: 154 start-page: 10 year: 2018 end-page: 17 ident: b0280 article-title: Prediction of soybean price in China using QR-RBF neural network model publication-title: Comput. Electron. Agric. – volume: 13 start-page: 284 year: 2009 end-page: 302 ident: b0110 article-title: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II publication-title: IEEE Trans. Evol. Comput. – volume: 41 start-page: 464 year: 2010 end-page: 470 ident: b0225 article-title: Predicting moisture content of agricultural products using artificial neural networks publication-title: Adv. Eng. Softw. – volume: 187 start-page: 95 year: 2015 end-page: 112 ident: b0285 article-title: Cross-validation for selecting a model selection procedure publication-title: J. Econom. – volume: 48 start-page: 2095 year: 2005 end-page: 2103 ident: b0045 article-title: Rate response assessment from various granular Vrt applicators publication-title: Trans. ASAE – volume: 65 start-page: 26 year: 2009 end-page: 35 ident: b0165 article-title: Modeling and optimization of parameters of flow rate of paddy rice grains through the horizontal rotating cylindrical drum of drum seeder publication-title: Comput. Electron. Agric. – reference: Tolis, P., 2017. Precision agriculture: future of the CAP? [WWW Document]. – volume: 100 start-page: 160 year: 2008 end-page: 166 ident: b0140 article-title: A study on the time response of a soil sensor-based variable rate granular fertiliser applicator publication-title: Biosyst. Eng. – volume: 221 start-page: 24 year: 2017 end-page: 31 ident: b0060 article-title: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm publication-title: Neurocomputing – volume: 67 start-page: 452 year: 2018 end-page: 466 ident: b0265 article-title: Differential evolution mutation operators for constrained multi-objective optimization publication-title: Appl. Soft Comput. J. – volume: 10 start-page: 1763 year: 2018 end-page: 1776 ident: b0130 article-title: Predictive analysis on responses in WEDM of titanium grade 6 using General Regression Neural Network (GRNN) and Multiple Regression Analysis (MRA) publication-title: Silicon – volume: 68 start-page: 268 year: 2018 end-page: 282 ident: b0245 article-title: Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization publication-title: Appl. Soft Comput. J. – volume: 144 start-page: 249 year: 2018 end-page: 259 ident: b0250 article-title: Numerical simulation of spreading performance and distribution pattern of centrifugal variable-rate fertilizer applicator based on DEM software publication-title: Comput. Electron. Agric. – volume: 184 start-page: 337 year: 2008 end-page: 351 ident: b0085 article-title: A statistical comparison of international fertiliser spreader test methods – confidence in bout width calculations publication-title: Powder Technol. – volume: 8 start-page: 19 year: 2015 end-page: 26 ident: b0200 article-title: Variable rate fertilization system with adjustable active feed-roll length publication-title: Int. J. Agric. Biol. Eng. – volume: 11 year: 2018 ident: b0115 article-title: Prediction of dissolved gas concentrations in transformer oil based on the KPCA-FFOA-GRNN model publication-title: Energies – volume: 73 start-page: 591 year: 2018 end-page: 606 ident: b0185 article-title: Modified differential evolution approach for practical optimal reactive power dispatch of hybrid AC–DC power systems publication-title: Appl. Soft Comput. J. – volume: 24 start-page: 3173 year: 2010 end-page: 3194 ident: b0090 article-title: Multi-objective planning model for large scale irrigation systems: method and application publication-title: Water Resour. Manag. – volume: 02 start-page: 71 year: 2016 end-page: 76 ident: b0030 article-title: Design and experiment of optimization control system for variable fertilization in winter wheat field based on fuzzy PID publication-title: J. Agric. Mach. – volume: 38 start-page: 201 year: 2018 end-page: 210 ident: b0100 article-title: Evaluation on overfertilization and its spatial-temporal difference about major grain crops in China publication-title: Econ. Geogr. – volume: 183 start-page: 29 year: 2018 end-page: 35 ident: b0240 article-title: Representative splitting cross validation publication-title: Chemom. Intell. Lab. Syst. – volume: 30 start-page: 675 year: 2015 end-page: 687 ident: b0105 article-title: Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for relay node deployment in wireless sensor networks publication-title: Appl. Soft Comput. J. – volume: 62 start-page: 86 year: 2018 end-page: 100 ident: b0160 article-title: Modeling of a greenhouse prototype using PSO and differential evolution algorithms based on a real-time LabView publication-title: Appl. Soft Comput. J. – volume: 13 start-page: 4253 year: 2013 end-page: 4263 ident: b0290 article-title: Multiobjective fireworks optimization for variable-rate fertilization in oil crop production publication-title: Appl. Soft Comput. J. – volume: 10 year: 2017 ident: b0145 article-title: The general regression neural network based on the fruit fly optimization algorithm and the data inconsistency rate for transmission line icing prediction publication-title: Energies – start-page: 50 year: 2014 end-page: 55 ident: b0155 article-title: Optimization of resistance spot welding parameters using differential evolution algorithm and GRNN publication-title: 2014 IEEE 8th Int. Conf. Intell. Syst. Control Green Challenges Smart Solut. ISCO 2014 – Proc. – reference: . – volume: 44 start-page: 1071 year: 2001 end-page: 1081 ident: b0050 article-title: Performance assessment and model development of a variable rate, spinner disc fertilizer applicator publication-title: Trans. ASAE – volume: 58 start-page: 700 year: 2017 end-page: 713 ident: b0295 article-title: A hybrid project scheduling and material ordering problem: Modeling and solution algorithms publication-title: Appl. Soft Comput. J. – volume: 94 start-page: 239 year: 2008 end-page: 250 ident: b0135 article-title: Optimisation of soil VIS-NIR sensor-based variable rate application system of soil phosphorus publication-title: Soil Tillage Res. – volume: 4 start-page: 360 year: 2017 end-page: 367 ident: b0055 article-title: Estimation of fertilizer usage from main crops in China publication-title: J. Agric. Resour. Environ. – volume: 9 start-page: 1 year: 2017 end-page: 28 ident: b0020 article-title: Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics publication-title: Sustain. – volume: 135 start-page: 405 year: 2017 end-page: 412 ident: b0205 article-title: Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks publication-title: Energy – volume: 70 start-page: 33 year: 2010 end-page: 41 ident: b0275 article-title: Gaussian processes based bivariate control parameters optimization of variable-rate granular fertilizer applicator publication-title: Comput. Electron. Agric. – volume: 57 start-page: 679 year: 2014 end-page: 687 ident: b0015 article-title: An image-based application rate measurement system for a granular fertilizer applicator publication-title: Trans. ASABE – volume: 71 start-page: 107 year: 2010 end-page: 127 ident: b0065 article-title: Development of soft computing and applications in agricultural and biological engineering publication-title: Comput. Electron. Agric. – volume: 122 start-page: 165 year: 2016 ident: 10.1016/j.compag.2019.105063_b0025 article-title: Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2016.05.061 – start-page: 381 year: 2014 ident: 10.1016/j.compag.2019.105063_b0080 article-title: Research and application of variable rate fertilizer applicator system based on a DC Motor – volume: 38 start-page: 201 year: 2018 ident: 10.1016/j.compag.2019.105063_b0100 article-title: Evaluation on overfertilization and its spatial-temporal difference about major grain crops in China publication-title: Econ. Geogr. – volume: 135 start-page: 405 year: 2017 ident: 10.1016/j.compag.2019.105063_b0205 article-title: Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks publication-title: Energy doi: 10.1016/j.energy.2017.06.089 – volume: 41 start-page: 464 year: 2010 ident: 10.1016/j.compag.2019.105063_b0225 article-title: Predicting moisture content of agricultural products using artificial neural networks publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2009.10.003 – volume: 65 start-page: 26 year: 2009 ident: 10.1016/j.compag.2019.105063_b0165 article-title: Modeling and optimization of parameters of flow rate of paddy rice grains through the horizontal rotating cylindrical drum of drum seeder publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2008.07.006 – volume: 26 start-page: 1129 year: 2015 ident: 10.1016/j.compag.2019.105063_b0150 article-title: A general regression neural network approach for the evaluation of compressive strength of FDM prototypes publication-title: Neural Comput. Appl. doi: 10.1007/s00521-014-1788-5 – volume: 11 start-page: 712 year: 2007 ident: 10.1016/j.compag.2019.105063_b0170 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – volume: 70 start-page: 33 year: 2010 ident: 10.1016/j.compag.2019.105063_b0275 article-title: Gaussian processes based bivariate control parameters optimization of variable-rate granular fertilizer applicator publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2009.08.009 – volume: 13 start-page: 284 year: 2009 ident: 10.1016/j.compag.2019.105063_b0110 article-title: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2008.925798 – volume: 2 year: 2010 ident: 10.1016/j.compag.2019.105063_b0125 article-title: ARM and DSP-based bivariable fertilizing control system design and implementation publication-title: J. Agric. Mach. – volume: 24 start-page: 3173 year: 2010 ident: 10.1016/j.compag.2019.105063_b0090 article-title: Multi-objective planning model for large scale irrigation systems: method and application publication-title: Water Resour. Manag. doi: 10.1007/s11269-010-9601-4 – volume: 184 start-page: 337 year: 2008 ident: 10.1016/j.compag.2019.105063_b0085 article-title: A statistical comparison of international fertiliser spreader test methods – confidence in bout width calculations publication-title: Powder Technol. doi: 10.1016/j.powtec.2007.09.004 – volume: 100 start-page: 160 year: 2008 ident: 10.1016/j.compag.2019.105063_b0140 article-title: A study on the time response of a soil sensor-based variable rate granular fertiliser applicator publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2008.03.007 – volume: 4 start-page: 360 year: 2017 ident: 10.1016/j.compag.2019.105063_b0055 article-title: Estimation of fertilizer usage from main crops in China publication-title: J. Agric. Resour. Environ. – volume: 67 start-page: 452 year: 2018 ident: 10.1016/j.compag.2019.105063_b0265 article-title: Differential evolution mutation operators for constrained multi-objective optimization publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2018.03.028 – ident: 10.1016/j.compag.2019.105063_b0220 – volume: 6 start-page: 19278 year: 2018 ident: 10.1016/j.compag.2019.105063_b0235 article-title: General regression neural network and artificial-bee-colony based general regression neural network approaches to the number of end-of-life vehicles in China publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2814054 – volume: 02 start-page: 71 year: 2016 ident: 10.1016/j.compag.2019.105063_b0030 article-title: Design and experiment of optimization control system for variable fertilization in winter wheat field based on fuzzy PID publication-title: J. Agric. Mach. – volume: 57 start-page: 679 year: 2014 ident: 10.1016/j.compag.2019.105063_b0015 article-title: An image-based application rate measurement system for a granular fertilizer applicator publication-title: Trans. ASABE – volume: 26 year: 2012 ident: 10.1016/j.compag.2019.105063_b0040 article-title: Design, development and field evaluation of a map-based variable rate granular fertilizer application control system publication-title: Contact Dermatitis – volume: 154 start-page: 10 year: 2018 ident: 10.1016/j.compag.2019.105063_b0280 article-title: Prediction of soybean price in China using QR-RBF neural network model publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.08.016 – volume: 10 year: 2017 ident: 10.1016/j.compag.2019.105063_b0145 article-title: The general regression neural network based on the fruit fly optimization algorithm and the data inconsistency rate for transmission line icing prediction publication-title: Energies doi: 10.3390/en10122066 – volume: 73 start-page: 591 year: 2018 ident: 10.1016/j.compag.2019.105063_b0185 article-title: Modified differential evolution approach for practical optimal reactive power dispatch of hybrid AC–DC power systems publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2018.08.038 – start-page: 450 year: 2010 ident: 10.1016/j.compag.2019.105063_b0120 article-title: A modified differential evolution algorithm and its application in thermal process model identification – volume: 62 start-page: 86 year: 2018 ident: 10.1016/j.compag.2019.105063_b0160 article-title: Modeling of a greenhouse prototype using PSO and differential evolution algorithms based on a real-time LabViewTM application publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2017.10.023 – volume: 48 start-page: 2095 year: 2005 ident: 10.1016/j.compag.2019.105063_b0045 article-title: Rate response assessment from various granular Vrt applicators publication-title: Trans. ASAE doi: 10.13031/2013.20086 – volume: 73 start-page: 56 year: 2010 ident: 10.1016/j.compag.2019.105063_b0075 article-title: Development and performance assessment of a DC electric variable-rate controller for use on grain drills publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2010.04.004 – volume: 172 start-page: 3028 year: 2018 ident: 10.1016/j.compag.2019.105063_b0210 article-title: Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2017.11.107 – volume: 71 start-page: 107 year: 2010 ident: 10.1016/j.compag.2019.105063_b0065 article-title: Development of soft computing and applications in agricultural and biological engineering publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2010.01.001 – volume: 144 start-page: 249 year: 2018 ident: 10.1016/j.compag.2019.105063_b0250 article-title: Numerical simulation of spreading performance and distribution pattern of centrifugal variable-rate fertilizer applicator based on DEM software publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2017.12.015 – volume: 221 start-page: 24 year: 2017 ident: 10.1016/j.compag.2019.105063_b0060 article-title: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.027 – volume: 94 start-page: 239 year: 2008 ident: 10.1016/j.compag.2019.105063_b0135 article-title: Optimisation of soil VIS-NIR sensor-based variable rate application system of soil phosphorus publication-title: Soil Tillage Res. doi: 10.1016/j.still.2006.07.016 – volume: 23 start-page: 341 year: 1997 ident: 10.1016/j.compag.2019.105063_b0195 article-title: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces publication-title: J. Glob. Optim. doi: 10.1023/A:1008202821328 – volume: 11 year: 2018 ident: 10.1016/j.compag.2019.105063_b0115 article-title: Prediction of dissolved gas concentrations in transformer oil based on the KPCA-FFOA-GRNN model publication-title: Energies – volume: 13 start-page: 4253 year: 2013 ident: 10.1016/j.compag.2019.105063_b0290 article-title: Multiobjective fireworks optimization for variable-rate fertilization in oil crop production publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2013.07.004 – volume: 114 start-page: 1164 year: 2016 ident: 10.1016/j.compag.2019.105063_b0070 article-title: Modeling of chemical exergy of agricultural biomass using improved general regression neural network publication-title: Energy doi: 10.1016/j.energy.2016.08.090 – volume: 15 start-page: 304 year: 2014 ident: 10.1016/j.compag.2019.105063_b0035 article-title: A model to analyze as-applied reports from variable rate applications publication-title: Precis. Agric. doi: 10.1007/s11119-014-9358-5 – volume: 9 start-page: 1 year: 2017 ident: 10.1016/j.compag.2019.105063_b0020 article-title: Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics publication-title: Sustain. – year: 2011 ident: 10.1016/j.compag.2019.105063_b0270 article-title: Bivariate fertilization control sequence optimization based on relevance vector machine publication-title: J. Agric. Mach. – volume: 38 start-page: 30 year: 2014 ident: 10.1016/j.compag.2019.105063_b0255 article-title: Predicting the maintenance cost of construction equipment: comparison between general regression neural network and Box-Jenkins time series models publication-title: Autom. Constr. doi: 10.1016/j.autcon.2013.10.024 – volume: 72 start-page: 87 year: 2010 ident: 10.1016/j.compag.2019.105063_b0010 article-title: Neural network prediction of performance parameters of an inclined plate seed metering device and its reverse mapping for the determination of optimum design and operational parameters publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2010.03.001 – volume: 100 start-page: 498 year: 2008 ident: 10.1016/j.compag.2019.105063_b0095 article-title: Fertiliser application performance of a variable-rate pneumatic granular applicator for rice production publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2008.05.007 – volume: 183 start-page: 29 year: 2018 ident: 10.1016/j.compag.2019.105063_b0240 article-title: Representative splitting cross validation publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2018.10.008 – volume: 68 start-page: 268 year: 2018 ident: 10.1016/j.compag.2019.105063_b0245 article-title: Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2018.03.053 – volume: 10 start-page: 1763 year: 2018 ident: 10.1016/j.compag.2019.105063_b0130 article-title: Predictive analysis on responses in WEDM of titanium grade 6 using General Regression Neural Network (GRNN) and Multiple Regression Analysis (MRA) publication-title: Silicon doi: 10.1007/s12633-017-9667-1 – volume: 44 start-page: 1071 year: 2001 ident: 10.1016/j.compag.2019.105063_b0050 article-title: Performance assessment and model development of a variable rate, spinner disc fertilizer applicator publication-title: Trans. ASAE doi: 10.13031/2013.6429 – volume: 187 start-page: 95 year: 2015 ident: 10.1016/j.compag.2019.105063_b0285 article-title: Cross-validation for selecting a model selection procedure publication-title: J. Econom. doi: 10.1016/j.jeconom.2015.02.006 – volume: 160 start-page: 31 year: 2019 ident: 10.1016/j.compag.2019.105063_b0005 article-title: Development and performance evaluation of a control system for variable rate granular fertilizer application publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.03.011 – volume: 85 start-page: 959 year: 2017 ident: 10.1016/j.compag.2019.105063_b0260 article-title: Disaster prediction model based on support vector machine for regression and improved differential evolution publication-title: Nat. Hazards doi: 10.1007/s11069-016-2613-5 – volume: 30 start-page: 675 year: 2015 ident: 10.1016/j.compag.2019.105063_b0105 article-title: Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for relay node deployment in wireless sensor networks publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2015.01.051 – start-page: 50 year: 2014 ident: 10.1016/j.compag.2019.105063_b0155 article-title: Optimization of resistance spot welding parameters using differential evolution algorithm and GRNN – volume: 113 start-page: 260 year: 2015 ident: 10.1016/j.compag.2019.105063_b0175 article-title: Field testing of an automatic control system for variable rate fertilizer application publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2015.03.003 – volume: 58 start-page: 700 year: 2017 ident: 10.1016/j.compag.2019.105063_b0295 article-title: A hybrid project scheduling and material ordering problem: Modeling and solution algorithms publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2017.05.030 – volume: 101 start-page: 411 year: 2008 ident: 10.1016/j.compag.2019.105063_b0215 article-title: Granular fertiliser application rate control system with integrated output volume measurement publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2008.09.019 – volume: 54 start-page: 455 year: 1999 ident: 10.1016/j.compag.2019.105063_b0190 article-title: Multicriterion decision making in irrigation planning publication-title: Irrig. Drain. doi: 10.1002/ird.197 – volume: 8 start-page: 19 year: 2015 ident: 10.1016/j.compag.2019.105063_b0200 article-title: Variable rate fertilization system with adjustable active feed-roll length publication-title: Int. J. Agric. Biol. Eng. – volume: 1 start-page: 93 year: 2017 ident: 10.1016/j.compag.2019.105063_b0230 article-title: A survey of multiobjective evolutionary algorithms based on decomposition
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Snippet	•An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was... To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which...
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SubjectTerms	Algorithms applicators Bivariate analysis Coefficient of variation Differential evolution (DE) Evolutionary algorithms Fertilization fertilizers General Regression Neural Network (GRNN) General regression neural networks Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Multiple objective analysis Optimal control Optimization Precision agriculture Response time Shafts (machine elements) Variable-rate fertilization
Title	MOEA/D-DE based bivariate control sequence optimization of a variable-rate fertilizer applicator
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