Machine learning model for nutrient release from biopolymers coated controlled-release fertilizer

Sayed Ameenuddin Irfan; Babar Azeem; Kashif Irshad; Salem Algarni; Kuzilati Kushaari; Saiful Islam; Mostafa A.H. Abdelmohimen

doi:10.3390/agriculture10110538

Machine learning model for nutrient release from biopolymers coated controlled-release fertilizer

Sayed Ameenuddin Irfan^*, Babar Azeem, Kashif Irshad^*, Salem Algarni, Kuzilati Kushaari, Saiful Islam, Mostafa A.H. Abdelmohimen

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Recent developments in the controlled-release fertilizer (CRF) have led to the new modern agriculture industry, also known as precision farming. Biopolymers as encapsulating agents for the production of controlled-release fertilizers have helped to overcome many challenging problems such as nutrients’ leaching, soil degradation, soil debris, and hefty production cost. Mechanistic modeling of biopolymers coated CRF makes it challenging due to the complicated phenomenon of biodegradation. In this study, a machine learning model is developed utilizing Gaussian process regression to predict the nutrient release time from biopolymer coated CRF with the input parameters consisting of diffusion coefficient, coefficient of-variance of coating thickness, coating mass thickness, coefficient of variance of size distribution and surface hardness from biopolymer coated controlled-release fertilizer. The developed model has shown greater prediction capabilities measured with R² equalling 1 and a Root Mean Square Error (RMSE) equalling 0.003. The developed model can be utilized to study the nutrient release profile of different biopolymers’-coated controlled-release fertilizers.

Original language	English
Article number	538
Pages (from-to)	1-13
Number of pages	13
Journal	Agriculture (Switzerland)
Volume	10
Issue number	11
DOIs	https://doi.org/10.3390/agriculture10110538
State	Published - Nov 2020
Externally published	Yes

Bibliographical note

Funding Information:
Funding: Funding for this work has been provided by the Deanship of Scientific Research, King Khalid University, Ministry of Education, Kingdom of Saudi Arabia, under research grant award number (R.G.P1./95/40).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

Biopolymer coating
Controlled-release fertilizer
Enzymatic degradation
Gaussian process regression
Machine learning
Modelling and simulation

ASJC Scopus subject areas

Food Science
Agronomy and Crop Science
Plant Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/agriculture10110538

Cite this

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title = "Machine learning model for nutrient release from biopolymers coated controlled-release fertilizer",

abstract = "Recent developments in the controlled-release fertilizer (CRF) have led to the new modern agriculture industry, also known as precision farming. Biopolymers as encapsulating agents for the production of controlled-release fertilizers have helped to overcome many challenging problems such as nutrients{\textquoteright} leaching, soil degradation, soil debris, and hefty production cost. Mechanistic modeling of biopolymers coated CRF makes it challenging due to the complicated phenomenon of biodegradation. In this study, a machine learning model is developed utilizing Gaussian process regression to predict the nutrient release time from biopolymer coated CRF with the input parameters consisting of diffusion coefficient, coefficient of-variance of coating thickness, coating mass thickness, coefficient of variance of size distribution and surface hardness from biopolymer coated controlled-release fertilizer. The developed model has shown greater prediction capabilities measured with R2 equalling 1 and a Root Mean Square Error (RMSE) equalling 0.003. The developed model can be utilized to study the nutrient release profile of different biopolymers{\textquoteright}-coated controlled-release fertilizers.",

keywords = "Biopolymer coating, Controlled-release fertilizer, Enzymatic degradation, Gaussian process regression, Machine learning, Modelling and simulation",

author = "Irfan, \{Sayed Ameenuddin\} and Babar Azeem and Kashif Irshad and Salem Algarni and Kuzilati Kushaari and Saiful Islam and Abdelmohimen, \{Mostafa A.H.\}",

note = "Funding Information: Funding: Funding for this work has been provided by the Deanship of Scientific Research, King Khalid University, Ministry of Education, Kingdom of Saudi Arabia, under research grant award number (R.G.P1./95/40). Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2020",

month = nov,

doi = "10.3390/agriculture10110538",

language = "English",

volume = "10",

pages = "1--13",

journal = "Agriculture (Switzerland)",

issn = "2077-0472",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

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TY - JOUR

T1 - Machine learning model for nutrient release from biopolymers coated controlled-release fertilizer

AU - Irfan, Sayed Ameenuddin

AU - Azeem, Babar

AU - Irshad, Kashif

AU - Algarni, Salem

AU - Kushaari, Kuzilati

AU - Islam, Saiful

AU - Abdelmohimen, Mostafa A.H.

N1 - Funding Information: Funding: Funding for this work has been provided by the Deanship of Scientific Research, King Khalid University, Ministry of Education, Kingdom of Saudi Arabia, under research grant award number (R.G.P1./95/40). Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020/11

Y1 - 2020/11

N2 - Recent developments in the controlled-release fertilizer (CRF) have led to the new modern agriculture industry, also known as precision farming. Biopolymers as encapsulating agents for the production of controlled-release fertilizers have helped to overcome many challenging problems such as nutrients’ leaching, soil degradation, soil debris, and hefty production cost. Mechanistic modeling of biopolymers coated CRF makes it challenging due to the complicated phenomenon of biodegradation. In this study, a machine learning model is developed utilizing Gaussian process regression to predict the nutrient release time from biopolymer coated CRF with the input parameters consisting of diffusion coefficient, coefficient of-variance of coating thickness, coating mass thickness, coefficient of variance of size distribution and surface hardness from biopolymer coated controlled-release fertilizer. The developed model has shown greater prediction capabilities measured with R2 equalling 1 and a Root Mean Square Error (RMSE) equalling 0.003. The developed model can be utilized to study the nutrient release profile of different biopolymers’-coated controlled-release fertilizers.

AB - Recent developments in the controlled-release fertilizer (CRF) have led to the new modern agriculture industry, also known as precision farming. Biopolymers as encapsulating agents for the production of controlled-release fertilizers have helped to overcome many challenging problems such as nutrients’ leaching, soil degradation, soil debris, and hefty production cost. Mechanistic modeling of biopolymers coated CRF makes it challenging due to the complicated phenomenon of biodegradation. In this study, a machine learning model is developed utilizing Gaussian process regression to predict the nutrient release time from biopolymer coated CRF with the input parameters consisting of diffusion coefficient, coefficient of-variance of coating thickness, coating mass thickness, coefficient of variance of size distribution and surface hardness from biopolymer coated controlled-release fertilizer. The developed model has shown greater prediction capabilities measured with R2 equalling 1 and a Root Mean Square Error (RMSE) equalling 0.003. The developed model can be utilized to study the nutrient release profile of different biopolymers’-coated controlled-release fertilizers.

KW - Biopolymer coating

KW - Controlled-release fertilizer

KW - Enzymatic degradation

KW - Gaussian process regression

KW - Machine learning

KW - Modelling and simulation

UR - https://www.scopus.com/pages/publications/85095985856

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DO - 10.3390/agriculture10110538

M3 - Article

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SN - 2077-0472

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ER -

Machine learning model for nutrient release from biopolymers coated controlled-release fertilizer

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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