Comparative Analysis and Calibration of Low Cost Resistive and Capacitive Soil Moisture Sensor

Chowdhury, Sourodip; Sen, Shaunak; Janardhanan, S.

doi:10.48550/arxiv.2210.03019

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Because the land has water and vice versa [47]. On the other side, knowledge of the soil's moisture content can help farmers make less mistakes [48]. One of the elements that influences soil fertility is the amount of water in the soil [49].…”

Section: ) Soil Moisture Field Data Valuesmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

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Section: ) Soil Moisture Field Data Valuesmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The latter are known to work at low frequencies, and measurements are affected by soil texture, temperature and salinity [22][23][24][25]. Extensive research has demonstrated that such sensors are not so prone to temperature and salinity factors if working in the soil temperature range 15-30 • C and for soils where soil solution electrical conductivity EC w is less than 10 dS/m [26]. However, as measurement frequency increases (around 50 MHz and above), this influence is reduced [27] (Meter Group, online technical note on soil moisture sensors).…”

Section: Introductionmentioning

confidence: 99%

Development and Application of an IoT-Based System for Soil Water Status Monitoring in a Soil Profile

Comegna,

Hassan,

Coppola

2024

Sensors

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Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ–h–K relationships. The cost of such commercially available sensors may vary over several orders of magnitude. In recent years, some sensors have been designed in the framework of Internet of Things (i.e., IoT) systems to make remote real-time soil data acquisition more straightforward, enabling low-cost field-scale monitoring at high spatio-temporal scales. In this paper, we introduce a new multi-parameter sensor designed for the simultaneous estimation of θ and h at different soil depths and, due to the sensor’s specific layout, the soil hydraulic conductivity function via the instantaneous profile method (IPM). Our findings indicate that a second-order polynomial function is the most suitable model (R2 = 0.99) for capturing the behavior of the capacitive-based sensor in estimating θ in the examined soil, which has a silty-loam texture. The effectiveness of low-cost capacitive sensors, coupled with the IPM method, was confirmed as a viable alternative to time domain reflectometry (TDR) probes. Notably, the layout of the sensor makes the IPM method less labor-intensive to implement. The proposed monitoring system consistently demonstrated robust performance throughout extended periods of data acquisition and is highly suitable for ongoing monitoring of soil water status.

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Comparative Analysis and Calibration of Low Cost Resistive and Capacitive Soil Moisture Sensor

Cited by 2 publications

References 11 publications

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Development and Application of an IoT-Based System for Soil Water Status Monitoring in a Soil Profile

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