Real-Time Nitrogen Regulation via IoT Edge Computing: A Chlorophyll Fluorescence-Driven Framework for Sustainable Plant Factories
Zhenning Sun, Yongxia Yang, Miao Lü, Huimin Li, Jie-Xiao Peng, Shi-Jie Tian, Jin Ping Hu, Pan Gao · IEEE Internet of Things Journal · 2025
Traditional nitrogen management systems in plant factories, based on static nutrient formulations, cause 30 50% nitrogen wastage and environmental pollution. Fixed threshold parameters fail to meet the dynamic needs of crops, reducing yield and quality, this study proposes an Internet of Things (IoT) closed-loop control system based on a dynamic nitrogen regulation model. By integrating Maximum Information Coefficient (MIC), Analytic Hierarchy Process (AHP), U-chord curvature method, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a multi-parameter dynamic weighting nitrogen regulation interval model was developed. This model overcomes the limitations of traditional methods that rely on fixed formulas or single parameters. The experiments demonstrated that the model can accurately identify the optimal nitrogen concentrations during the seedling stage (5.0 8.0 mmol/L) and the maturity stage (11.0 14.0 mmol/L), reducing nitrogen fertilizer usage by 36.2% 49.7%, while increasing fresh weight by 12% and leaf potassium and phosphorus contents by 4.8% 19.5%. The system employs an edge-cloud collaborative architecture, with the Raspberry Pi 4B serving as the edge node for real-time regulation (response time. 0.5 seconds), supporting remote monitoring and model updates, forming a "perception-decision-execution" closed-loop. This research provides a nitrogen management paradigm for smart agriculture that combines dynamic precision with engineering practicality, which can be extended to vertical farms and urban agricultural scenarios.