International Journal of Technology and Applied Science

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A Review on Machine Learning Approaches for Sustainable Fertilizer Optimization and Smart Agriculture

Author(s) Mohit Thakre, Adarsh Hajare, Muskan Goriya, Deepak Lokhande, Prof. Satish Charokar
Country India
Abstract Sues applied to fertilizer recommendation systems, emphasizing models such as Decision Trees, Ran stainable 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 main- tain soil health. This paper presents a comprehensive review of machine learning approach dom 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 introduced 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.
Field Engineering
Published In Volume 16, Issue 11, Array 2025
Published On 2025-11-14
Cite This A Review on Machine Learning Approaches for Sustainable Fertilizer Optimization and Smart Agriculture - Mohit Thakre, Adarsh Hajare, Muskan Goriya, Deepak Lokhande, Prof. Satish Charokar - IJTAS Volume 16, Issue 11, Array 2025.
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