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