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

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Listed:
  • 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)

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.

Suggested Citation

  • Yugong Dang & Hongen Ma & Jun Wang & Zhigang Zhou & Zhidong Xu, 2022. "An Improved Multi-Objective Optimization Decision Method Using NSGA-III for a Bivariate Precision Fertilizer Applicator," Agriculture, MDPI, vol. 12(9), pages 1-23, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1492-:d:917632
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    References listed on IDEAS

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    2. Alamgir Khalil & Salahuddin & Wali Khan Mashwani & Muhammad Shafiq & Saima Hassan & Wiyada Kumam, 2021. "New advanced outliers detection tests," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(7), pages 1640-1655, April.
    3. Manfredo Guilizzoni & Paloma Maldonado Eizaguirre, 2022. "Trend Lines and Japanese Candlesticks Applied to the Forecasting of Wind Speed Data Series," Forecasting, MDPI, vol. 4(1), pages 1-17, January.
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    Cited by:

    1. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.

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