AgriTwin-Sim: An Interactive Digital Twin Framework for AI-Driven Smart Farming

Abstract

Modern agriculture faces unprecedented challenges requiring innovative solutions for enhanced productivity and sustainability. Digital Twins (DTs), coupled with Artificial Intelligence (AI) and the Internet of Things (IoT), offer a transformative paradigm for creating dynamic virtual replicas of agricultural systems. This paper introduces an advanced interactive web-based Digital Twin framework designed to bridge the gap between virtual sensor data and intelligent, actionable insights for smart farming. The framework integrates a high-fidelity simulation core, encompassing sophisticated mathematical models for environmental dynamics and plant biophysiology, with a robust AI engine capable of complex analysis, prediction, and decision support. Simulated data from a virtual sensor network feeds the DT, enabling the AI to perform real-time assessments, identify anomalies, predict future states, and recommend optimized management actions. The entire system is exposed through an intuitive and visually rich web interface that facilitates interactive exploration, scenario analysis, and direct visualization of the DT's evolution and the AI's reasoning. We demonstrate the framework's architecture, the formal underpinnings of its key components, and its utility as a powerful tool for research, development, and operational management in next-generation agriculture, showcasing a seamless pathway from data acquisition to intelligent intervention.