Area Recognition Based on Grid Structure for Rice Paddy Monitoring Using Wireless Sensor Network

In the era of precision agriculture, effective and efficient monitoring of rice paddy fields is vital for optimizing crop yield and ensuring environmental sustainability. This study presents a novel approach utilizing a grid-structured Wireless Sensor Network (WSN) to monitor key environmental parameters such as soil moisture, temperature, and methane gas concentration. The novelty of this research lies in the detailed implementation of a grid-based sensor system that provides comprehensive coverage, enhanced data precision, and integration with artificial intelligence (AI) for predictive analysis. This system not only enables realtime monitoring but also generates actionable insights through a color-coded alert system that signals abnormal conditions such as excessive heat or harmful gas levels. The study demonstrates significant improvements in resource efficiency, including water management and reduced chemical usage, resulting in higher crop yields. The results highlight the potential for grid-based WSN systems to drive sustainable agriculture, offering precise interventions based on real-time environmental data.