Re-Calibration of Model-Based Capacitive Sensor for IoT Soil Moisture Measurements
Abstract
Low-cost automatic irrigation systems require quality calibrated soil moisture sensors. The sensor is an indirect method of soil moisture measurement. The sensor works based on the change in the dielectric constant. So, it requires to be calibrated in terms of the soil water content. Polynomial and linear models are frequently used to calibrate soil moisture sensor data in the gravimetric test method. However, computational effort is required. This study aims to obtain a sensor calibration application that can provide the best model of the available models for model-based capacitive soil moisture sensor. This research was conducted using primary data from gravimetric test experiment on Internet of things (IoT) based soil moisture sensor. Web-based re-calibration application produced best model based on adjusted R Squared. Finally, model-based capacitive soil moisture sensor set up using best model coefficient. The results show that the web-based re-calibration application can provide the best model for model-based capacitive soil moisture sensor. Based on gravimetric test experiments and web applications, the best model is a polynomial regression model order 3 with 0.945 adjusted R Squared. The model predicted value for soil moisture is in the range 0 – 1.2 for raw sensor data values of 100 – 530. When the model coefficient configured in capacitive soil moisture sensor and Blynk application, soil moisture measurement can be done via mobile phone in real time.
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References
E. A. A. D. Nagahage, I. S. P. Nagahage, and T. Fujino, “Calibration and validation of a low-cost capacitive moisture sensor to integrate the automated soil moisture monitoring system,” Agric., vol. 9, no. 7, Jul. 2019, doi: 10.3390/AGRICULTURE9070141.
L. Chen et al., “Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors,” 2019, doi: 10.3390/s19204381.
B. P. C. Silva, D. Tassinari, M. L. N. Silva, B. M. Silva, N. Curi, and H. R. da Rocha, “Nonlinear models for soil moisture sensor calibration in tropical mountainous soils,” Sci. Agric., vol. 79, no. 4, 2022, doi: 10.1590/1678-992X-2020-0253.
J. Brenner, G. Genova, G. Bertoldi, G. Niedrist, and S. Della Chiesa, “SWCalibrateR: Interactive, Web – Based Calibration of Soil Moisture Sensors,” J. Open Res. Softw., vol. 7, pp. 1–7, 2019, doi: 10.5334/JORS.254.
I. Setiawan, J. Junaidi, F. Fadjryani, and F. R. Amaliah, “Automatic Plant Watering System for Local Red Onion Palu using Arduino,” J. Online Inform., vol. 7, no. 1, pp. 28–37, Jun. 2022, doi: 10.15575/JOIN.V7I1.813.
I. Setiawan, Junaidi, Fadjryani, and F. R. Amaliah, Mobile App for Plant Watering System with Verticulture Planting Technique. Atlantis Press International BV, 2023. doi: 10.2991/978-94-6463-228-6.
R Core Team, “R: A Language and Environment for Statistical Computing.” Vienna, Austria, 2023. [Online]. Available: https://www.r-project.org/
W. Chang et al., “shiny: Web Application Framework for R.” 2022. [Online]. Available: https://shiny.rstudio.com/
M. Pramanik et al., “Automation of soil moisture sensor-based basin irrigation system,” Smart Agric. Technol., vol. 2, Dec. 2022, doi: 10.1016/J.ATECH.2021.100032.
A. Shukla et al., “Soil Moisture Estimation using Gravimetric Technique and FDR Probe Technique : A Comparative Analysis,” no. January, pp. 89–92, 2014.
J. Hrisko, “Capacitive Soil Moisture Sensor Theory, Calibration, and Testing,” 2020, doi: 10.13140/RG.2.2.36214.83522.
V. Ogwo, K. N. Ogbu, C. C. Anyadike, O. A. Nwoke, and C. C. Mbajiorgu, “Development and testing of a capacitive digital soil moisture sensor with printed circuit board as a probe,” Niger. J. Technol., vol. 39, no. 3, pp. 911–917, Sep. 2020, doi: 10.4314/NJT.V39I3.33.
I. Setyowati, D. Novianto, and E. Purnomo, “Preliminary design and soil moisture sensor yl-69 calibration for implementation of smart irrigation,” J. Phys. Conf. Ser., vol. 1517, no. 1, May 2020, doi: 10.1088/1742-6596/1517/1/012078.
G. S. Campbell et al., “Method A: Soil-Specific Calibrations For Meter Soil Moisture Sensors,” metergroup, 2023. http://publications.metergroup.com/Sales and Support/METER Environment/Website Articles/Method_a_soil_specific_calibrations_for_meter_soil_moisture_sensors.pdf (accessed Aug. 22, 2023).
K. Iriyanta, B. Purnomosidi, and W. Andriyani, “IoT-Based Soil Moisture Monitoring And Soil Moisture Prediction Using Linear Regression ( Case Study of Vinca Plants ),” Journal-of-Intelligent-Software-Systems-2962-7702, vol. 2, no. 1, 2023, doi: 10.26798/jiss.v2i1.929.
E. Ostertagová, “Modelling using polynomial regression,” Procedia Eng., vol. 48, pp. 500–506, 2012, doi: 10.1016/j.proeng.2012.09.545.
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