International Scientific Journal of Engineering and Management

An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata
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ISSN: 2583-6129

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SAYFOS: An IOT and Machine Learning–Driven Platform for Sustainable Crop Yield and Fertilizer Optimization

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Published 9 April 2026
Updated 9 April 2026

SAYFOS: An IOT and Machine Learning–Driven Platform for Sustainable Crop Yield and Fertilizer Optimization

 

 

 

 

Dr.S.Surya Kumari1 , Kotaru Vyshnavi2 , Kandra Rishendra3 ,Devandla Nithin4 , Machala GirishKumar5
1Assistant Professor Dept of Information Technology, SV College of Engineering, Tirupati, India.
2B.Tech, Dept of Information Technology, SV College of Engineering, Tirupati, India.
3B.Tech, Dept of Information Technology, SV College of Engineering, Tirupati, India.
4B.Tech, Dept of Information Technology, SV College of Engineering, Tirupati, India.
5B.Tech, Dept of Information Technology, SV College of Engineering, Tirupati, India.

 

 

 

 

Abstract-The modernization of agriculture necessitates intelligent systems that improve crop yield predictions and optimize fertilizer application while promotingsustainability. Traditional farming methods and current  smart agriculture solutions often fall short due to fragmented approaches and limited real-time integration of diverse agricultural data. This paper introduces SAYFOS (Smart Agriculture Yield and Fertilizer Optimization System), a comprehensive platform leveraging Internet of Things (IoT) technology and machine learning to enhance farming efficiency.SAYFOS integrates multi-source agricultural data—soil health, weather conditions, crop management—andemploys advanced feature selection techniques to build accurate predictive models for crop yield and fertilizer needs. The system continuously monitors and refines its predictions, offering real-time recommendations that optimizeresource usage.  Experimental resultsdemonstrate SAYFOS’s capability to increase crop yield by up to 25%, reduce water consumption by 33%, and improve fertilizer efficiency by 25% compared to conventionalmethods.Additionally,SAYFOS minimizes environmental impact by preventing fertilizer overuse, lowers production costs, and reduces labor requirements. The system’s scalability and adaptability to varied crops applicability and in environments suggest broad precision represents a significant agriculture. SAYFOS advancement toward sustainable, intelligent farming practices, supporting farmers worldwide in achieving higher productivity and environmental conservation through data-driven decisions.

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