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Musa, Mohammad Dahlan Th.
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Computer Science & IT

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Re-Calibration of Model-Based Capacitive Sensor for IoT Soil Moisture Measurements Setiawan, Iman; Musa, Mohammad Dahlan Th.; Putri, Saskia Amalia
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6809

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.
ROVIGA: Model-Driven Soil Moisture Sensor for Internet-Connected Plant Pot Setiawan, Iman; Musa, Mohammad Dahlan Th.; Nurrahma, Andi; Alfina, Alfina; Rachman, Rohis; Ariza, Moh
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8599

Abstract

The soil moisture sensor provides numerical measurements to detect changes in soil moisture using an analog voltage output. This research aims to develop a capacitive sensor based on a statistical model to detect soil moisture for plant watering, leveraging the Internet of Things (IoT). The analysis was conducted using polynomial and linear regression models. The modeling process was based on primary gravimetric test results from dried soil. The best model coefficients, selected based on the highest adjusted R-squared value, were used for sensor recalibration. A watering system was then developed using an Arduino and a model-driven capacitive soil moisture sensor integrated into an internet-connected smart plant pot, enabling remote control via a mobile phone. The research findings indicate that the 8th-order polynomial model, with the highest adjusted R-squared value of 0.9583, is the most accurate. The smart watering system using the model-driven capacitive sensor achieved soil moisture prediction outcomes ranging from 0.08 to 1.01 for 150 to 418 sensor data points. The internet-connected smart plant pot allows precise and real-time control, delivering notifications and enabling actions when plants require watering.
Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor Setiawan, Iman; Musa, Mohammad Dahlan Th.; Afriza, Dini Aprilia; Hafidah, Siti Nur
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8905

Abstract

Measuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user but lacks in accuracy. This research aims to compare and identify the best machine learning model that can improve indirect measurement (soil moisture sensor prediction) using direct measurement (gravimetric test) as a response variable. This research uses linear regression, K-Nearest Neighbours (KNN) and Decision Tree models. The three models were then compared based on Root Mean Square Error (RMSE). The results suggested that KNN (0.02939128) had the smallest RMSE value followed by decision tree (0.05144186) and linear regression model (0.05172371).
Co-Authors Afriza, Dini Aprilia Alfina, Alfina Ariza, Moh Hafidah, Siti Nur Iman Setiawan Nurrahma, Andi Putri, Saskia Amalia Rachman, Rohis