@article{scholars12551,
           title = {Machine learning model for nutrient release from biopolymers coated controlled-release fertilizer},
            year = {2020},
             doi = {10.3390/agriculture10110538},
           pages = {1--13},
          volume = {10},
         journal = {Agriculture (Switzerland)},
       publisher = {MDPI AG},
          number = {11},
            note = {cited By 7},
          author = {Irfan, S. A. and Azeem, B. and Irshad, K. and Algarni, S. and Kushaari, K. and Islam, S. and Abdelmohimen, M. A. H.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095985856&doi=10.3390\%2fagriculture10110538&partnerID=40&md5=74064f191e90339f824ffd617826905d},
        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{\^a}?? 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{\^a}??-coated controlled-release fertilizers. {\^A}{\copyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
            issn = {20770472}
}