Smart Agriculture Management System Using Virtual IOT and AI Decision Support
DR. S. Gnanapriya1, Anzul Ahammed.S2
1Associate professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
ncmdrsgnanapriya@nehrucolleges.com
2Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
anzulahammed02@gmail.com
Abstract
Modern agriculture increasingly depends on data-driven decision support to improve productivity and resource efficiency; however, most existing smart farming solutions rely heavily on physical IoT sensor infrastructures that are costly, difficult to maintain, and limited in scalability for experimentation and deployment in resource-constrained environments. This dependency restricts widespread adoption and reduces accessibility to intelligent farming technologies. To address these limitations, this paper presents SAMS, a Virtual IoT and AI-based Smart Agriculture Management System designed to provide integrated decision support without mandatory reliance on physical sensing hardware. The proposed system introduces a virtual sensor simulation layer that generates realistic environmental data streams, enabling continuous monitoring, analytics, and system evaluation under diverse agricultural conditions. The platform incorporates multiple AI-driven modules, including crop recommendation, yield prediction, disease risk assessment, and weather-based analysis, all integrated within a unified architecture supported by centralized data processing and an interactive analytics dashboard. System implementation demonstrates stable data flow, consistent decision outputs, and effective integration of AI models within a scalable software framework. Experimental evaluation confirms that the virtual IoT approach can support intelligent agricultural decision-making while significantly reducing infrastructure constraints. The proposed system provides a practical, flexible, and cost-efficient alternative for smart agriculture applications, particularly suitable for educational, experimental, and early deployment scenarios.
Keywords- Virtual IoT Simulation, Smart Agriculture, Artificial Intelligence, Machine Learning, Crop Monitoring, Precision Agriculture, Decision Support Systems.
