Comparative Analysis of Filtering Techniques for Efficient Data Aggregation in Smart Hydroponic Systems

Abstract

The rapid evolution of digital technologies has transformed traditional agriculture into a more efficient and
intelligent system. This study focuses on the development of a data aggregation strategy in Smart Hydroponic Systems to
optimize network consumption without sacrificing data accuracy. By utilizing Threshold Filtering and Delta-Based
Filtering techniques, significant reductions in data transmission were achieved while maintaining essential temperature
monitoring capabilities. Experimental results demonstrated that both techniques effectively reduced data points and
optimized bandwidth usage, particularly in temperature-stable environments. This research contributes to the
advancement of sustainable digital agriculture by providing an efficient approach to managing network resources in IoTbased
hydroponic
systems.