An Improved Multi-Objective Optimization Decision Method Using NSGA-III for a Bivariate Precision Fertilizer Applicator

Yugong Dang, Hongen Ma, Jun Wang (), Zhigang Zhou and Zhidong Xu
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Yugong Dang: School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471000, China
Hongen Ma: School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471000, China
Jun Wang: School of Electrical Engineering, Henan University of Science and Technology, Luoyang 471000, China
Zhigang Zhou: School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471000, China
Zhidong Xu: China Petroleum First Construction Corporation Ltd., Luoyang 471023, China

Agriculture, 2022, vol. 12, issue 9, 1-23

Abstract: In order to boost the performance of a bivariable granular fertilizer applicator and simplify the control methodology of fertilization rate regulation, this paper proposed a fertilization decision method to obtain the optimal combination of rotational speed and opening length by selecting the accuracy, uniformity, adjustment time, and breakage rate as the optimization objectives. We processed the outlier data collected using the indoor bench test, segmented the data with the fertilization growth rate as the index, and proved the rationality of the data segmentation by an independent sample t -test. SVM, BPNN, ELM, and RVM were used to train the two data sections to create the fertilization rate prediction model, and the models with the highest accuracy in the two data sections were selected for the assembly of the final prediction model used to describe the fertilization process of the bivariate fertilizer applicator. Moreover, the fertilization performance problem model was established with the objectives of accuracy, uniformity, adjustment time, and breakage rate and was solved using the NSGA-III algorithm to gain an optimal fertilization decision. Compared with GA and MOEA-D-DE methods, the results show that, using the new method, the average relative error declines from 8.64% and 6.05% to 3.09%, and the average coefficient of variation reduces from 6.67% and 6.81% to 6.41%, respectively. In addition, the adjustment time lowers from 2.01 s and 1.33 s to 0.78 s, and the average breakage rate drops from 1.084% and 0.845% to 0.803%, respectively. It is indicated that the presented method offers the most notable improvements in accuracy and adjustment time, while the advancements in regard to uniformity and breakage rate is slight, but both are within a reasonable range.

Keywords: granular fertilizer applicator; fertilization decision; multi-objective optimization; NSGA-III; breakage rate (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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