Smart Irrigation Robot Based on Moisture and Temperature Sensors Using Fuzzy Logic with Wireless Monitoring

Abstract

Efficient water management in agriculture is critical to addressing climate variability and resource scarcity. Existing automatic irrigation systems often lack adaptability to dynamic environmental conditions, and many do not integrate multi-sensor data for precise water delivery. This study aims to develop and implement an adaptive automatic plant watering system using a two-axis cartesian robot integrated with temperature and soil moisture sensors. The proposed system applies fuzzy logic for decision-making to optimize watering duration and frequency based on environmental conditions. Monitoring and control are enabled through an IoT-based web dashboard built with Node-RED, allowing real-time observation and management of irrigation processes. Experimental results indicate that the robot’s movements on the x and y axes achieve a small positioning error of 3.08%, and soil moisture levels increased by an average of 94.83% after watering. Furthermore, the fuzzy logic-based control achieves an irrigation decision accuracy of 92.5% when both temperature and moisture data are utilized, compared to 90.5% accuracy without temperature input. These results demonstrate that integrating temperature data enhances the system’s performance in managing water delivery precisely and efficiently. The developed smart irrigation system offers a promising solution to support sustainable agriculture by conserving water and optimizing plant care in various environmental conditions.