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Application of artificial neural network for predicting nutrient release from neem (azadirachtaindica) coated urea

• Manish Vashishtha
• Gaurav Yadav
• Meenu Suhag
• Shiv Om Meena

(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024

https://doi.org/10.7148/2024-0423

Manish vashishtha, Gaurav yadav, Meenu suhag, Shiv om meena (2024). Application of Artificial Neural Network for predicting nutrient release from Neem (Azadirachtaindica) Coated Urea, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0423

Population spike is driving up food consumption, while urbanization is clearly causing soil fertility to decline linearly, increasing production costs and reliance on conventional fertilizers. Alternatives, such as controlled-release fertilizers (CRF) like Neem-coated urea fertilizers (NCU) have several benefits, are required to address these issues.  To forecast the release of nutrients from NCU, this labor-intensive research uses mathematical modeling, with a focus on developing and analyzing mathematical models that use ANN. Utilizing a GRNN to compute the amount of nutrients released from NCU, the contours of nitrogen release were predicted. By using a UV-Vis spectrophotometer, the nitrogen release contours were estimated experimentally in the lab. The findings demonstrated the striking similarity between the GRNN model's projected and experimental values. In addressing nonlinear prediction issues, the GRNN model performed remarkably well in creating NCU.