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 and Mostafa A. H. Abdelmohimen
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Sayed Ameenuddin Irfan: Shale Gas Research Group (SGRG), Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
Babar Azeem: Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
Kashif Irshad: Center of Research Excellence in Renewable Energy (CoRE-RE), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Salem Algarni: College of Engineering, Mechanical Engineering Department, King Khalid University, Abha 61413, Saudi Arabia
KuZilati KuShaari: Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
Saiful Islam: Department of Geotechnical & Transportation, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johar Bahru 81310, Malaysia
Mostafa A. H. Abdelmohimen: College of Engineering, Mechanical Engineering Department, King Khalid University, Abha 61413, Saudi Arabia

Agriculture, 2020, vol. 10, issue 11, 1-13

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 R 2 equalling 1 and a Root Mean Square Error ( R M S E ) equalling 0.003. The developed model can be utilized to study the nutrient release profile of different biopolymers’-coated controlled-release fertilizers.

Keywords: controlled-release fertilizer; biopolymer coating; enzymatic degradation; machine learning; gaussian process regression; modelling and simulation (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: 2020
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