A Hybrid Approach to Data Mining from IoT and WSN Systems for Precision Agriculture Decision Support
Keywords:
Agricultural Analytics, Crop Monitoring, Data Mining, Decision Support System, Edge Computing, Energy-Efficient Data Processing, IoT, Machine Learning, Precision Agriculture, Smart Farming, Wireless Sensor Networks, Yield Prediction.Abstract
Precision agriculture integrates advanced technologies to optimize crop production, resource management, and sustainability. Wireless Sensor Networks (WSNs) and IoT continuously collect large-scale agricultural data, but extracting actionable insights from this data poses significant challenges. This research proposes a hybrid data mining framework combining machine learning, statistical analysis, and pattern recognition to process real-time data from Io T-enabled WSNs. The system addresses key issues such as energy-efficient data collection, noise reduction, feature selection, and predictive analytic for yield estimation, disease detection, and irrigation
management.
References
D. Mekala and P. Viswanathan, “A novel smart irrigation system using IoT and cloud computing for precision agriculture,” Computers and Electronics in Agriculture, vol. 135, pp. 295–307, 2017.
H. Jawad, R. Nordin, S. Gharghan, A. Jawad, and M. Ismail, “Energy-efficient wireless sensor networks for precision agriculture: A review,” Sensors, vol. 17, no. 8, pp. 1781, 2017.
