Real-Time Farm Simulation Models for Training and Research Using AI

摘要

The integration of artificial intelligence (AI) in real-time farm simulation models offers a transformative approach to enhancing agricultural training and research. Traditional farming methods often rely on static models, limiting the scope for real-world variability and predictive accuracy. This study presents a novel AI-driven farm simulation framework designed to mimic real-time farming conditions, incorporating environmental, crop, and resource management data. By leveraging machine learning algorithms and dynamic data streams, the system predicts crop performance, resource utilization, and potential risks, providing actionable insights to researchers and practitioners. The results demonstrated that the AI-driven simulation achieved a predictive accuracy of 93.4% for crop yield, with a 20% improvement in decision-making efficiency compared to existing static models. Additionally, the system’s interactive interface and real-time updates significantly enhanced user engagement and learning outcomes. The framework also facilitated scenario testing, allowing users to evaluate the impact of various farming strategies under different environmental conditions. This research establishes a scalable and robust platform for improving agricultural education and decision-making, addressing critical challenges in resource optimization and sustainable farming practices. Future work will focus on expanding the system's datasets and incorporating advanced AI techniques to enhance predictive capabilities further