IoT BASED SOIL MOISTURE MANAGEMENT USING CAPACITIVE SENSOR AND USER-FRIENDLY SMARTPHONE APPLICATION

Sarmphim, Pharunee; SUTTHIPHON, Narongsak; JAROENSONG, Parimate; SIRISATHITKUL, Chitnarong; Sirisathitkul, Yaowarat

doi:10.35633/inmateh-66-16

INMATEH

2022

DOI: 10.35633/inmateh-66-16

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IoT BASED SOIL MOISTURE MANAGEMENT USING CAPACITIVE SENSOR AND USER-FRIENDLY SMARTPHONE APPLICATION

Pharunee Sarmphim

Narongsak SUTTHIPHON

Parimate JAROENSONG

et al.

Abstract: The Internet of Things (IoT) based system was assembled to monitor the moisture of soils for both indoor and outdoor uses. The SKU:SEN0193 capacitive soil moisture sensor exhibited a linear response to a variation in water volume added to the soil. Microcontroller Arduino NodeMCU was used with ESP8266 Wi-Fi module to transfer the sensing data in real-time, and the soil moisture data was displayed by the Blynk application on a smartphone. When the moisture dropped under the pre-defined threshold, the user was i… Show more

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Designing and Calibration of a Low-Cost Multi-Point Soil Moisture Monitoring System for Precision Agriculture

CHERECHES,

GASPAR,

DANCI

2024

INMATEH

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Soil moisture monitoring and control are essential aspects for precision agriculture. The paper presents the designing and calibration of a low-cost soil moisture monitoring system. The system includes 28 capacitive sensors, connected to an Arduino based data acquisition system, allowing simultaneous multi-point measurements. The calibration process was conducted on six reference points within the moisture content range of 0–25%. The calibration results indicate a non-linear variation and reveal a significant deviation between the sensors leading to the determination of individual variation curves for each sensor.

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Designing and Calibration of a Low-Cost Multi-Point Soil Moisture Monitoring System for Precision Agriculture

CHERECHES,

GASPAR,

DANCI

2024

INMATEH

View full text Add to dashboard Cite

show abstract

Development of Fogponics Cultivation System for Microgreens With Internet of Things Monitoring System and Machine Learning Automation

SARANGANI,

SOMERA-ALMEROL,

CINENSE

et al. 2024

INMATEH

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New technologies are emerging every day to improve the productivity of food production to meet rising demands. Microgreens have gained popularity nowadays and are known for being nutritious and easy to cultivate. Fogponics is one of the emerging technologies that atomizes the nutrient solutions into fine mist, improving the oxygenation and reduces water usage that lacks from traditional farming methods. The study developed an automated fogponics system for microgreens production using machine learning automation and internet of things monitoring systems. The model's evaluation output proves that the system is reliable and capable of predicting an appropriate direction given the datasets acquired from temperature and humidity while the plants are thriving over time. The system has successfully reduced the temperature fluctuation ranging from 26°-33°C to 27°-30°C and stabilized humidity levels from 75-100% to 90-96%. As a result, the performance of the model effectively yielded the microgreens to flourish in its environmental parameters by incorporating machine learning automation and IoT-based monitoring systems. This study strengthened the importance of contributing a promising alternative for sustainable microgreens production. This prototype represents its significant advancement in agricultural strategies for indoor microgreens cultivation, offering a potential alternative for efficient and scalable production.

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IoT BASED SOIL MOISTURE MANAGEMENT USING CAPACITIVE SENSOR AND USER-FRIENDLY SMARTPHONE APPLICATION

Cited by 2 publications

References 31 publications

Designing and Calibration of a Low-Cost Multi-Point Soil Moisture Monitoring System for Precision Agriculture

Designing and Calibration of a Low-Cost Multi-Point Soil Moisture Monitoring System for Precision Agriculture

Development of Fogponics Cultivation System for Microgreens With Internet of Things Monitoring System and Machine Learning Automation

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