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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Published in:Computers and electronics in agriculture Vol. 167; p. 105063
Main Authors: Zhang, Jiqin, Liu, Gang, Luo, Chengming, Hu, Hao, Huang, Jiayun
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2019
Elsevier BV
Subjects:
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 Access:Get full text
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Summary:•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.
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ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2019.105063