Abstract
With the rapid development of precision agriculture technology, simulating and optimizing rice paddy production processes using big data and artificial intelligence (AI) has become an important research direction. This study proposes a virtual rice paddy laboratory framework based on big data and AI, integrating multi-source data such as soil, climate, and crop growth to establish a prediction and optimization model for nitrogen use efficiency (NUE). Using the virtual laboratory, the rice paddy production process under different soil types and climate conditions was simulated, and various water and fertilizer management strategies were designed and evaluated for their impact on NUE. Based on advanced machine learning algorithms, a dynamic simulation model for crop growth and nitrogen utilization was developed, and its prediction accuracy was validated, leading to the optimization of water and fertilizer management plans. Simulation results indicate that NUE can be significantly improved under different soil conditions through targeted adjustments in management strategies. This study presents a low-cost and efficient optimization method for paddy field management, providing technical support for the future intelligent development of precision agriculture.
