Fertilizer Strength Prediction Model Based on Shape Characteristics

The accurate prediction of fertilizer strength can reduce the crushing rate in the process of transportation and utilization, ensure the efficient utilization of fertilizer, so as to realize the sustainable and clean production of crops. To achieve this goal, for the first time, a fertilizer strengt...

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Autores principales: Hongjian Zhang, Chunbao Xu, Jinxing Wang
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
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Acceso en línea:https://doaj.org/article/7a055e73130c4e42bfa4f0330a2e55f5
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spelling oai:doaj.org-article:7a055e73130c4e42bfa4f0330a2e55f52021-11-19T00:06:48ZFertilizer Strength Prediction Model Based on Shape Characteristics2169-353610.1109/ACCESS.2021.3068147https://doaj.org/article/7a055e73130c4e42bfa4f0330a2e55f52021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9383278/https://doaj.org/toc/2169-3536The accurate prediction of fertilizer strength can reduce the crushing rate in the process of transportation and utilization, ensure the efficient utilization of fertilizer, so as to realize the sustainable and clean production of crops. To achieve this goal, for the first time, a fertilizer strength prediction model based on the shape characteristics is proposed to this paper by the support vector machine and the improved differential evolution algorithm. Firstly, the fertilizer shape characteristics and strength are measured by the independently developed agricultural material shape analyzer and the digital strength tester. Secondly, a fertilizer strength prediction model based on a support vector machine is constructed, in which the optimal combined kernel function is proposed according to the Pearson correlation coefficient and the normal distribution law. Finally, a differential evolution algorithm is improved to optimize the internal parameters of the fertilizer strength prediction model. The experiment results show that the maximum error rate of the fertilizer strength prediction model is between −5% and 5%. The proposed method of this paper can lay a solid foundation for fertilizer production and quality inspection, which will help reduce fertilizer crushing, improve fertilizer utilization to realize the sustainable and clean production of crops.Hongjian ZhangChunbao XuJinxing WangIEEEarticleFertilizer strength prediction modelshape characteristicssupport vector machinecombined kernel functionimproved differential evolution algorithmElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 87007-87023 (2021)
institution DOAJ
collection DOAJ
language EN
topic Fertilizer strength prediction model
shape characteristics
support vector machine
combined kernel function
improved differential evolution algorithm
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Fertilizer strength prediction model
shape characteristics
support vector machine
combined kernel function
improved differential evolution algorithm
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Hongjian Zhang
Chunbao Xu
Jinxing Wang
Fertilizer Strength Prediction Model Based on Shape Characteristics
description The accurate prediction of fertilizer strength can reduce the crushing rate in the process of transportation and utilization, ensure the efficient utilization of fertilizer, so as to realize the sustainable and clean production of crops. To achieve this goal, for the first time, a fertilizer strength prediction model based on the shape characteristics is proposed to this paper by the support vector machine and the improved differential evolution algorithm. Firstly, the fertilizer shape characteristics and strength are measured by the independently developed agricultural material shape analyzer and the digital strength tester. Secondly, a fertilizer strength prediction model based on a support vector machine is constructed, in which the optimal combined kernel function is proposed according to the Pearson correlation coefficient and the normal distribution law. Finally, a differential evolution algorithm is improved to optimize the internal parameters of the fertilizer strength prediction model. The experiment results show that the maximum error rate of the fertilizer strength prediction model is between −5% and 5%. The proposed method of this paper can lay a solid foundation for fertilizer production and quality inspection, which will help reduce fertilizer crushing, improve fertilizer utilization to realize the sustainable and clean production of crops.
format article
author Hongjian Zhang
Chunbao Xu
Jinxing Wang
author_facet Hongjian Zhang
Chunbao Xu
Jinxing Wang
author_sort Hongjian Zhang
title Fertilizer Strength Prediction Model Based on Shape Characteristics
title_short Fertilizer Strength Prediction Model Based on Shape Characteristics
title_full Fertilizer Strength Prediction Model Based on Shape Characteristics
title_fullStr Fertilizer Strength Prediction Model Based on Shape Characteristics
title_full_unstemmed Fertilizer Strength Prediction Model Based on Shape Characteristics
title_sort fertilizer strength prediction model based on shape characteristics
publisher IEEE
publishDate 2021
url https://doaj.org/article/7a055e73130c4e42bfa4f0330a2e55f5
work_keys_str_mv AT hongjianzhang fertilizerstrengthpredictionmodelbasedonshapecharacteristics
AT chunbaoxu fertilizerstrengthpredictionmodelbasedonshapecharacteristics
AT jinxingwang fertilizerstrengthpredictionmodelbasedonshapecharacteristics
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