NodeMCU and Blynk aided Advanced Water Quality Monitoring Set-up

Joshi, Shreya Suhas; Uttarwar, Gouri; Sawlani, Payal; Adlakhe, Ram

doi:10.29322/ijsrp.10.04.2020.p10062

Cited by 5 publications

(2 citation statements)

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“…By using an IoT platform, data can be monitored and displayed via a mobile phone app and then used to analyse the behaviour and effect of pH and temperature of water towards surrounding conditions [17][18][19]. A water quality monitoring system has been successfully developed using designed hardware, software, and architecture.…”

Section: Resultsmentioning

confidence: 99%

Investigation on Water Quality for Farmed Aquatic Species by IoT Monitoring System

Syazwan Izharuddin Mohamad Sabri,

Norazlianie Sazali,

Ahmad Shahir Jamaludin

et al. 2023

ARASET

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The objective of this research is to optimize fish growth in super-intensive aquaculture systems by addressing water quality issues. Changes in water parameters can significantly impact fish growth and even lead to mortality. To overcome this problem, real-time monitoring of water quality parameters is crucial, and the data should be automatically transmitted to the entrepreneur through the Internet of Things (IoT). This allows the owner to access real-time data remotely, eliminating the need to be physically present at the fish cage. By monitoring the data on a smartphone, the owner can efficiently manage the water quality. In this study, a water quality monitoring system utilizing temperature, pH, and ESP32, with the parameter values displayed on the Blynk platform was developed. The system successfully measures real-time water conditions and transmits the data to the Blynk application. The test results demonstrate the system's capability to receive and send data, but further analysis is needed to evaluate the accuracy and reliability of the measurements and identify any noteworthy findings.

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Section: Resultsmentioning

confidence: 99%

Investigation on Water Quality for Farmed Aquatic Species by IoT Monitoring System

Syazwan Izharuddin Mohamad Sabri,

Norazlianie Sazali,

Ahmad Shahir Jamaludin

et al. 2023

ARASET

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“…It was used as a Wi-Fi module and microcontroller in this prototype. It was used to collect data using digital and analogue pins from various sensors (Joshi et al, 2015). After calibrating those data points, the collected data was sent to the Arduino Pro Mini.It was used to stream data from the field with the help of external devices such as mobile phones or computers (Mehendale, 2022).…”

Section: Node Mcu Esp8266 and Esp32cammentioning

confidence: 99%

Enhancing agricultural productivity through a semi-autonomous IOT robot in smart farming systems

Yeasdani,

Bhuiyan,

Ifte

et al. 2023

Bangladesh J. Agri.

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In addressing the challenge of enhancing agricultural productivity in developing countries, this research introduces a semi-autonomous IoT robot designed to modernize traditional farming practices in regions like Bangladesh. The study explores whether such a robot can effectively integrate with existing farming practices and assesses its impact on agricultural productivity, resource optimization, and most importantly cost-efficiency. The literature reveals a push towards smart farming technologies, but their adoption in less affluent regions is hindered by cost and resource constraints. Employing a mixed-methods case study approach, the research developed and tested a robot equipped with an NPK sensor for detecting levels of nitrogen, phosphorus, and potassium in the soil, a water level indicator to measure flood water levels in millimeters, and a soil moisture sensor. These data were transmitted to the user's phone over the internet, allowing for remote monitoring of fertilizers and water levels. Additionally, the system included a remote-controllable water dispenser for irrigation and a fruit-picking mechanism for harvesting. The results indicated that all intended data collection was executed accurately, enabling users to remotely monitor soil conditions and effectively control the robot's actions. However, the initial cost of the robot may be slightly expensive for individual farmers, though mass production is anticipated to reduce the price to a level that is reasonably affordable for widespread adoption. However, limitations in sensor calibration for different soil types are acknowledged. Future research suggested exploring sensor calibration precision, extending system capabilities, and integrating predictive AI for a comprehensive agricultural solution. Bangladesh J. Agri. 2023, 48(2): 95-105

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