%I MDPI AG
%V 10
%A S.A. Irfan
%A B. Azeem
%A K. Irshad
%A S. Algarni
%A K. Kushaari
%A S. Islam
%A M.A.H. Abdelmohimen
%T Machine learning model for nutrient release from biopolymers coated controlled-release fertilizer
%P 1-13
%X 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
%N 11
%R 10.3390/agriculture10110538
%D 2020
%J Agriculture (Switzerland)
%L scholars12551
%O cited By 7