Comparative Study on Sensitivity Variations in Three Soil Moisture Sensors to Optimize Water Use Efficiency in IOT-Based Automated Irrigation

Authors

  • Nelson Makange Sokoine University of Agriculture
  • Leonard Mwankemwa Sokoine University of Agriculture
  • Evance Kabyazi School of Engineering and Technology, Box 976 Musoma –Tanzania
  • Charles Kajanja Sokoine University of Agriculture
  • Gervas Lusele Sokoine University of Agriculture
  • Goodlight Valentine Sokoine University of Agriculture
  • Evance Chaima Lilongwe University of Agriculture and Natural Resources

DOI:

https://doi.org/10.64383/irjss.JAN250104

Keywords:

Internet of Things, moisture, microcontroller, real-time monitoring, sensors

Abstract

Efficient water management in agriculture is crucial for improving productivity. In this study, Automated irrigation systems using soil moisture sensors for precise water discharge control and Internet of Things (IoT) technology were studied to achieve real-time data monitoring. The sensitivity of different types of soil moisture sensors varies, especially in field conditions. Hence, poses a challenge in optimizing irrigation water, leading to lowered productivity. Therefore, we provided insights into optimizing sensor selection and calibration for more effective water resource management in agriculture through performance evaluation of capacitive, resistive, and Time Domain Reflectometry (TDR) sensors in measuring soil moisture content under different soil types. The correlation between sensor sensitivity and the accuracy of soil moisture measurements under different soil types was studied. The laboratory experiment was conducted to evaluate th-e performance of factory-based calibrated soil moisture sensors. The performance of the soil moisture sensors was evaluated using Root Mean Squared Error (RMSE), Index of Agreement (IA), and Mean Bias Error (MBE). The result shows that the performance of the factory-based calibrated capacitive, resistive, and Time Domain Reflectometry (TDR) did not meet all the statistical criteria except the capacitive sensor for sand loamy. There was a strong positive relationship among sensors. The correlation between TDR and resistive moisture readings was 0.96, between TDR and capacitive moisture readings was 0.98, and between resistive and capacitive moisture readings was 0.97. The correction equations were developed using the laboratory experiment and validated in the field. The correction equations for capacitive, resistive, and TDR improved the accuracy in field conditions.

Author Biographies

Nelson Makange, Sokoine University of Agriculture

School of Engineering and Technology, Box 3003, Morogoro-Tanzania

Leonard Mwankemwa, Sokoine University of Agriculture

School of Engineering and Technology, Box 3003, Morogoro-Tanzania

Evance Kabyazi, School of Engineering and Technology, Box 976 Musoma –Tanzania

University of Agriculture and Technology

Charles Kajanja, Sokoine University of Agriculture

School of Engineering and Technology, Box 3003, Morogoro-Tanzania

Gervas Lusele, Sokoine University of Agriculture

School of Engineering and Technology, Box 3003, Morogoro-Tanzania

Goodlight Valentine, Sokoine University of Agriculture

School of Engineering and Technology, Box 3003, Morogoro-Tanzania

Evance Chaima, Lilongwe University of Agriculture and Natural Resources

Faculty of Life Sciences and Natural Resources, Box 143, Lilongwe –Malawi

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Soil Moisture Sensors to Optimize Water Use

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Published

2025-01-27

How to Cite

Makange, N., Mwankemwa, L., Kabyazi, E., Kajanja, C., Lusele, G., Valentine, G., & Chaima, E. (2025). Comparative Study on Sensitivity Variations in Three Soil Moisture Sensors to Optimize Water Use Efficiency in IOT-Based Automated Irrigation. International Research Journal of Scientific Studies, 2(1), 25–44. https://doi.org/10.64383/irjss.JAN250104

Issue

Vol. 2 No. 1 (2025): January

Section

Research Papers

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