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
Materias:	Fertilizer strength prediction model shape characteristics support vector machine combined kernel function improved differential evolution algorithm Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/7a055e73130c4e42bfa4f0330a2e55f5
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id	oai:doaj.org-article:7a055e73130c4e42bfa4f0330a2e55f5
record_format	dspace
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
_version_	1718420617570549760

Fertilizer Strength Prediction Model Based on Shape Characteristics

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