Intelligent IoT Sensing Systems for Real-Time Irrigation Optimization

Abstract

Irrigation takes up a significant portion of the total global use of freshwater, and traditional irrigation systems are not efficient because they are not dynamically timed, slow to provide feedback, and lose the ability to respond to real-time soil and climate variability. These restrictions cause impoverished water consumption, unbalanced crop irrigation, energy consumption, and less sustainability of agriculture. This paper is intended to design and test an intelligent IoT sensing system to optimize irrigation in real time to increase water-use efficiency without jeopardizing optimal crop health. Its main purposes are to allow 24/7 monitoring of the fields and make irrigation decisions based on data and reduce the human input (by automation). The suggested methodology combines low-cost internet of Things sensors of soil moisture, temperature, and humidity and the environmental parameters with the edge-assisted data processing and cloud-based analytics. The sensor information is sent through a wireless network to an intelligent decision layer whereby a rule based fuzzy logic algorithm is used to dynamically control the irrigation time and frequency in response to real-time field conditions. Machine learning models are also used to analyze historical data to increase the predictive accuracy and system flexibility. It has been experimentally evaluated that the proposed system can reduce water consumption by 28-35 %, respond to irrigation faster by 40 % and enhance crop water-use efficiency by about 22 % relative to time-based irrigation systems. There were more than 95% system reliability and decision accuracy at varying environments. The smart IoT sensing architecture offers a solution that is effective, scalable, and low-energy consumption-based optimization of real-time irrigation to enable sustainable agriculture and climate-resilient farming methods.