Machine Learning Model for Nutrient Release from Biopolymers Coated Controlled-Release Fertilizer

• Main Authors: Sayed Ameenuddin Irfan, Babar Azeem, Kashif Irshad, Salem Algarni, KuZilati KuShaari, Saiful Islam, Mostafa A. H. Abdelmohimen
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Agriculture
• Subjects: controlled-release fertilizer; biopolymer coating; enzymatic degradation; machine learning; gaussian process regression; modelling and simulation
• Online Access: https://www.mdpi.com/2077-0472/10/11/538

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 <inline-formula><math display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> equalling 1 and a Root Mean Square Error (<inline-formula><math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>) equalling 0.003. The developed model can be utilized to study the nutrient release profile of different biopolymers’-coated controlled-release fertilizers.