Sustainable Fertilizer Usage and Optimization for High Yield and Smart Agriculture

Author(s)	Mohit Thakre, Adarsh Hajare, Muskan Goriya, Deepak Lokhande, Prof. Satish Charokar
Country	India
Abstract	Sustainable agriculture has become one of the most critical challenges of modern times due to excessive fertilizer use, soil degradation, and climate change. Machine learning (ML) has emerged as a promising technology to optimize fertilizer application, improve yield, and maintain soil health. This paper presents a comprehensive review of machine learning approaches applied to fertilizer recommendation systems, emphasizing models such as De- cision Trees, Random Forests, Support Vector Machines, and Neural Networks. The review highlights the evolution of data-driven fertilizer optimization, compares previous systems, and discusses their limitations. Further, a proposed hybrid ML-based methodology is in- troduced to overcome the shortcomings of existing models by integrating Random Forest and real-time data analytics using web and cloud technologies. The paper concludes that intelligent, adaptive, and region-specific fertilizer management systems can significantly contribute to sustainable farming and higher crop productivity.
Keywords	Fertilizer Optimization, Sustainable Agriculture, Machine Learning, Smart Farm- ing, Random Forest, Precision Agriculture, Next.js, Flask
Discipline	Engineering
Published In	Volume 4, Issue 2, March-April 2026
Published On	2026-03-19

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Advanced International Journal of Multidisciplinary Research