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Performance Evaluation of Resistivity-Based Soil Moisture Sensors for IoT Based Real-Time Monitoring Systems

Year: 2025
Type: article
Abstract: The Internet of Things (IoT) has emerged as an outstanding innovation in agriculture, enabling precise data collection through digital electronics and wireless communications. The rising consumer demand for organic agriculture and sustainable practices requires continuous monitoring of soil and plant conditions for effective crop management. This project highlights the development and evaluation of an IoTenabled soil moisture monitoring system aiming to investigate the effectiveness of three commercial resistivity-type soil moisture sensors. The system was implemented using an ESP32 microcontroller and assessed on five soil samples with relative humidity (RH) levels of 0%, 20%, 40%, 60%, and 90%. The sensors' analogue signals were digitized, transmitted via Wi-Fi, and visualized in real-time using the Blynk IoT platform, which is widely accessible on smartphones and desktops. Experimental results demonstrated that sensor performance varied according to stability, sensitivity, and response time. The ABTEST-03 sensor exhibited superior voltage stability with 0.0387 V/%RH sensitivity, attributable to its integrated LM393 comparator module, whereas the ABTEST-01 sensor showed an enhanced sensitivity of 0.0535 V/%RH, likely due to its gold-plated probes that enhance conductivity and resist corrosion. The ABTEST-02 sensor achieved the most satisfactory reaction time, averaging 6 seconds for both rise and fall transitions. Despite minor discrepancies, the findings reveal the strengths and weaknesses of stability, sensitivity, and responsiveness among evaluated sensors. The study emphasizes the importance of precise sensor selection for efficient IoT-based irrigation systems and highlights the detailed characterization of commercial sensors, offering valuable insights for improved smart agriculture efficiency and resource optimization.
DOI: 10.24191/jmeche.v14i1.8408
Author: Rozina Abdul Rani
Language: English
Subfield: Plant Science
Open Access status: bronze
OpenAlex ID: W7108329003
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