Construction of a Technology System for Monitoring and Precision Maintenance of Garden Plant Growth Status Based on Internet of Things Sensing

The integration of sensing materials into garden monitoring addresses the high resource consumption and maintenance delays caused by insufficient real-time growth information and inaccurate management protocols. As wireless electromagnetic sensing and distributed communication technologies become increasingly important for intelligent environmental monitoring, this study constructs a technology system for monitoring and precision maintenance of garden plant growth based on Internet of Things (IoT) sensing. The system employs multi-source sensor nodes to collect soil moisture, temperature, ambient light, air humidity, and carbon dioxide concentration data, while a Wireless Sensor Network (WSN) enables real-time data transmission and collaborative perception. A data fusion model is adopted to extract multidimensional growth features and identify plant status, and a hybrid decision-making framework integrating fuzzy control and machine learning is developed to automatically regulate irrigation volume, fertilization frequency, and light intensity. In a pilot application in Suzhou, Jiangsu Province, the monitoring error was maintained within ±2.3%, data latency remained below 1.5 s, the leaf area increase rate improved by 12.6%, and water and fertilizer consumption decreased by 18.4% and 16.8%, respectively. The proposed system enables dynamic perception and precision maintenance of garden plant growth while providing effective technical support for intelligent ecological management and electromagnetic sensing-assisted environmental monitoring.