IoT-based Water Quality Monitoring Research Articles

Enhanced Hydroponic Farming with Crop Suitability Prediction

Hydroponics, a soil-less cultivation technique, offers efficient resource utilization and enhanced crop yields. However, precise water quality control is essential to ensure optimal nutrient absorption and successful crop growth. This research paper presents a comprehensive IoT-based water quality monitoring system specifically designed for hydroponics, incorporating pH, turbidity, and temperature sensors. The system can acquire data from sensors, data processing, and data visualization in real time. The sensor data is seamlessly transmitted from Arduino to ESP8266 and securely stored on the ThingSpeak cloud platform. Utilizing web scraping techniques, the collected data is extracted from ThingSpeak and seamlessly integrated into a user-friendly website. On the website, a machine learning (ML) model, trained on required data, automatically processes the realtime sensor readings and performs crop suitability predictions. The system enables continuous and automated monitoring of key water quality parameters, ensuring optimal conditions for hydroponic crop growth. The deployed ML model demonstrates remarkable performance in predicting water suitability for different crops. The model provides real-time predictions on the website by utilizing the data, empowering hydroponic farmers with data-driven insights to optimize crop selection based on water quality. To evaluate the system’s efficacy, extensive experimentation, and validation have been conducted using hydroponic setups with various crop types.

Development of an IoT Based Water Quality Monitoring Device for Domestic Fish Ponds

This study focuses on developing an affordable IoT-based water quality monitoring system for domestic fish ponds. The system aims to enable remote monitoring of critical water parameters, offering real-time data access through mobile or web interfaces. It includes an alert system to notify the pond owners of any significant changes in water quality, allowing swift corrective action. The initiative stems from challenges faced by aquaculture farmers due to insufficient knowledge about water pH levels. Understanding pH's importance, especially within the optimal range of 6.5-9.0 for fish culture, is crucial for success. Tests conducted on the system's performance in detecting various pH levels across different pond environments demonstrated its reliability in identifying low and normal pH levels. However, anomalies were observed in detecting higher pH levels, indicating potential sensitivity limitations that need further investigation for system refinement. While the system excelled in detecting low and normal pH levels accurately, improvements are required for detecting higher pH thresholds to ensure comprehensive monitoring across diverse water conditions. This enhancement is crucial for effective fish pond management and reducing losses for aquaculture farmers.

Crab Monitoring System (CMS) using Internet of Things (IoT’s)

Mangrove crabs are one of the leading fishery commodities with high economic value and have the potential to remap the direction of industrial development that combines ecological, social and economic interests. The majority of mud crab demand is met from natural fishing (±61.6%). One of the crises in mud crab production is related to the low cultivation capacity (±38.4%), even though new techniques have emerged in crab cultivations. This IoT-based water quality monitoring system was developed with a transmiting and receiving system. The Transmitting system is equipped with essential sensors needed for crabs to grow, such as sensors for air temperature, water temperature, humidity, pH, DO, and tides. The receiving system can displayed the data and transmit to the cloud and data processed by other users according to their scientific field.

IOT Based Water Quality Monitoring System

Abstract: Technologies are expanding and developing quickly across a wide range of disciplines as science continues to advance and improve. The improvement of human existence and modernization are the main goals of these developments. It is crucial to recognize that this advancement has come at the expense of making environmental issues worse. Pollution of water is one of the main threats in recent times as drinking water is getting contaminated and polluted. The polluted water can cause various diseases to humans and animals, which in turn affects the life cycle of the ecosystem [1]. The ESP32 microcontroller, along with DS18B20 Digital temperature sensor, turbidity sensor, and pH sensor, are used in the IoT-based water quality monitoring system presented in this project to continuously monitor and assess the water's quality. The ESP32 is programmed using the Arduino IDE software, and the Blynk IoT platform is used for data visualization and remote monitoring. Water temperature, turbidity levels, and pH values may be continuously monitored by the system, and users can access and analyze the data remotely using the Blynk mobile app or web dashboard. It is possible to study historical data patterns and set up alerts to inform users of any changes in the quality of the water. By enabling proactive actions to be performed to maintain and improve water quality while ensuring environmental protection, this system offers an efficient and effective method for managing water quality.

IOT BASED WATER QUALITY MONITORING SYSTEM

Water is one of the most vital resources for life, and protecting its quality is crucial for both the environment's health and the well-being of people. Monitoring and preserving the quality of the water is now essential given the mounting problems caused by pollution, climate change, and expanding populations. In this environment, the Internet of Things (IoT) has emerged as a ground-breaking innovation that has the potential to revolutionize the way we manage and monitor water quality. An IoT-based water quality monitoring system's relevance, components, advantages, and prospective applications are highlighted. It offers a chance to proactively address problems with water quality, protecting both the environment and the general people. Sensors used in IoT-based water quality monitoring systems can measure a wide range of factors, including temperature, dissolved oxygen, turbidity, pH levels, and chemical concentrations. These sensors are interfaced with the microcontroller unit. The sensors are positioned in pipelines, treatment facilities, or water bodies. Through wireless communication protocols like Wi-Fi or cellular networks, the data gathered by sensors is sent to a central server or cloud platform. This makes it possible to access and analyses data in real time. Users can use online or mobile applications to get the data on the quality of the water. These intuitive user interfaces offer up-to-the-minute data, historical trends, and alarms if water quality measurements differ from predetermined criteria. Urban populations can have access to safe drinking water by using IoT systems at water treatment facilities to monitor and manage water quality indicators. Aquaculture farms' production is increased by IoT- based systems' assistance in maintaining ideal water conditions for fish and other aquatic species. Monitoring soil and water quality helps farmers improve irrigation operations, which increases agricultural yields and decreases water waste. IoT is an excellent instrument for preserving water resources, defending the environment, and delivering real-time data, affordable solutions, and data-driven decision-making. Key Words: Microcontroller, Pumping station, Sensor, IoT, data, aquaculture.

Internet of Things untuk Sistem Pemantauan Kualitas Air pada Kolam Ikan Lele pada Pembudidaya TDR Sultan Adam Banjarmasin

TDR Catfish Farmer Sultan Adam Banjarmasin is a home industry (IRT) engaged in the business of catfish farming located on Jl. Sultan Adam No.17 RT.22, Surgi Mufti, North Banjarmasin. In the management of catfish farming, water quality is an important factor in the success of cultivation where poor water quality can cause fish to be more susceptible to disease. Apart from these needs, the TDR of Sultan Adam Catfish Farmers actually has problems in the process of monitoring pond water quality conditions which are currently carried out manually and periodically by breeders. This process is considered ineffective because it is difficult to determine water quality from the physical condition of the water which changes rapidly due to weather or fish feed residue. The solution offered to solve this problem is to develop a tool that can monitor temperature conditions and the acidity of catfish pond water automatically and in real time. This innovation was developed by utilizing the Internet of Things (IoT) through the use of the DS18B20 temperature sensor and SS15 pH sensor on the ESP32 WROOM-32D microcontroller. The results of system testing from fuzzy logic calculations at the output of the microcontroller and Matlab and the suitability of expert information in determining pool water quality obtained an average error value of 0.46%. Based on these results, it can be concluded that the IoT-based water quality monitoring system in determining water quality is suitable for direct use.

Design and Implementation of an Embedded Edge-Processing Water Quality Monitoring System for Underground Waters

Global warming effects are seen around the world and Latin American countries are not an exception, especially for expanding drought areas. Therefore, underground water resources use in the region is incrementing exponentially. However, temporal and spatial underground water information concerning availability and quality is scarce, disabling proper decision-making. In order to close that breach, we propose and Embedded Edge-processing IoT-based Water Quality Monitoring system. This letter introduces the design and implementation of this solution, specifically targeted to monitor irrigation and drinking water extracted from water wells. The system is designed to be deployed in central Chile, considering the topographic conditions, which severely affect power availability and communication resources. The captured data is stored in a data lake, for further processing according to water quality models.

Implementation of an IoT-Based Water Quality Monitoring System for Aquaculture

Implementation of an IoT-Based Water Quality Monitoring System for Aquaculture

DESAIN PERANGKAT BERBASIS IoT UNTUK PEMANTAUAN DAN PENGENDALIAN KUALITAS AIR PADA SISTEM IRIGASI RUMAH KACA

Because hydroponic plant nutrient is dissolved in the water, water quality management is essential for growing healthy plants. Taking care of the total dissolved solids (TDS) and the pH (water acidity) is essential. The purpose of this research is to create a IoT-based water quality monitoring device. The average TDS after 30 days of automation with an Arduino UNO was 1162.82 ppm, and the gadget kicked on the nutrient pump whenever the TDS fell below 1000 ppm or the EC fell below 2. The mechanism self-adjusts the pH when it falls below 5.5 or rises above 6.5, with a mean of 6.17.

A hybrid machine learning and embedded IoT-based water quality monitoring system

Since access to clean and safe water is one of life's most basic needs, the exponential growth of the world's population makes it vital to guarantee a workable framework. Manually collecting samples and sending them to a research center for discovery and analysis is the typical approach for gathering information on water qualities. However, this approach is illogical in the long run because it requires a lot of time and labor. This paper aimed to construct, test, and evaluate the usefulness of machine learning and the Internet of Things (IoT) at water storage stations. First, we developed a system prototype and assessed its performance using classifier and reliability matrices. This study considers water's physical and chemical parameters to evaluate the level of water pollutants present in drinking water. The parameters measured include temperature, pH, turbidity, Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Oxidation Reduction Potential (ORP), and electrical conductivity. After analyzing the sensor data, we used Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning algorithms to forecast the impurity level of the water measured. The performance showed that the ANN models used have the highest accuracy and are the most suitable to predict water source and status. We also introduced a water treatment method to provide an automated corrective measure based on a specific amount of water contamination. Based on the system's results, we concluded that AI and IoT are more efficient in remotely monitoring safe and harmful water conditions.

IoT Based Water Quality Monitoring System

Water pollution is the biggest problem nowadays. It is increasing as new technology evolves and human population increases. Evolving technologies makes human life easy. But they have huge impact on the environment, and one of the main problems is water pollution, which causes various diseases and other issues in living beings. So, it has to be monitored continuously and reduced as much as possible to ensure the provision of clean drinking water. This research presents the solution to monitor the water quality parameters like water conductivity, turbidity, pH continuously and send these values to the LCD and nearby devices using Wi-Fi module. Moreover, this system is designed to measure the quantity of water being monitored using water flow sensor. Water quality parameters are measured by different sensors such as pH, TDS, and Turbidity, which are connected to microcontroller (Arduino Uno). The proposed system uses GSM module to send the message to the officials during abnormal conditions. Hardware results of the proposed system are also provided in this work.

Design and Analysis Performance of IoT-Based Water Quality Monitoring System using LoRa Technology

Water quality is one of the most important variables impacting human life. Generally, water quality measurements must be conducted on-site. If the region to be investigated is large, several test locations will be required. Repeated evaluations of water quality will be complicated and time-consuming. Therefore, a real-time water quality monitoring system is required to protect and monitor the water in order to take proactive measures for contamination. This project focuses on the aforementioned concerns and uses LoRa technology and the Node-RED application to develop environmental sensors that monitor and display water quality. It is the measurement and collection of data on water quality parameters, including temperature, electric conductivity, pH, air quality, and turbidity, according to the region requiring analysis. The microcontroller processes the sensor data before transmitting it via the wireless network to the database, where it is displayed on the Node-RED dashboard. The experimental results demonstrated that a range of 2.0 km can be used to transmit information in areas where LoRa technology encounters obstacles. Furthermore, the IoT-based monitoring system is able to monitor water quality in real time and display a Node-RED dashboard. It was determined that usability assessments were more efficient and convenient.

Designing and Implementing IoT-Based Water Quality Monitoring and Control System of a Pilot Scale Deep Flow Technique Aquaponics for Enhanced Crop-Fish Production

Aquaponics is an agricultural model that combines aquaculture and hydroponic cultivation systems, which can be a solution for expanding agricultural land in urban areas. The success of an aquaponics system heavily relies on maintaining optimal pond water quality, which serves as a vital source of nutrition for plants and ensures the survival of fish. Many studies have been conducted to build a system that maintains the good quality of pond water, but much of it only utilizes a limited number of parameters. This study was conducted to develop a robust control system capable of monitoring and regulating the water quality parameters, temperature, pH, dissolved oxygen (DO), and total dissolved solids (TDS), in real-time using IoT based system and fuzzy logic as a control method for decision-making. Users can monitor the condition of the pond using web interfaces that can be accessed via desktop or mobile devices. The design implementation results on the DFT aquaponics system showed an increase in the growth of paddy crops and tilapia with precision data recorded on the cloud server of 94.11%.

Design of IoT Based Water Monitoring System (Simonair) For Arwana Fish Cultivation

Arwana fish is an ornamental fish that has high aesthetic value and is popular with Indonesian people. Silver Arowana is a type of freshwater ornamental fish originating from Brazil and belonging to the Osteoglossum genus. Problems in arowana fish farming include water quality, disease, nutrition and spawning (breeding). An important problem in enlarging silver arowana fish seeds is one of the maintenance media. Water quality is an external factor that plays a role in supporting fish growth. Water quality management has been one of the inhibiting factors in aquaculture, because it requires time, procedures and several stages to assess aquaculture water quality parameters, so farmers are late in handling the fish which results in death. Monitoring water quality that is practical, efficient and easy according to the development of IOT-based information technology 4.0 is urgently needed by cultivators to control fish in real time so that fish survival can be optimized. The research will be carried out for two years, namely 2022 and 2023. The purpose of the first phase of the research is to create an IOT-based Water Quality Monitoring System (SIMONAIR), while the second stage of research aims to apply the use of SIMONAIR to support the growth of arowana fish. The research was conducted at the Vocational School of IPB and in collaboration with a partner, namely CV Viez Aquatics. Phase 1 research will focus on the manufacture and calibration of SIMONAIR. Phase 2 research The use of the SIMONAIR application on silver arowana fish given different feed treatments. The overall research results are a technology package for silver arowana fish cultivation with high technology for the community or fish farmers

Design of IoT Based Water Monitoring System (Simonair) For Arwana Fish Cultivation

Arwana fish is an ornamental fish that has high aesthetic value and is popular with Indonesian people. Silver Arowana is a type of freshwater ornamental fish originating from Brazil and belonging to the Osteoglossum genus. Problems in arowana fish farming include water quality, disease, nutrition and spawning (breeding). An important problem in enlarging silver arowana fish seeds is one of the maintenance media. Water quality is an external factor that plays a role in supporting fish growth. Water quality management has been one of the inhibiting factors in aquaculture, because it requires time, procedures and several stages to assess aquaculture water quality parameters, so farmers are late in handling the fish which results in death. Monitoring water quality that is practical, efficient and easy according to the development of IOT-based information technology 4.0 is urgently needed by cultivators to control fish in real time so that fish survival can be optimized. The research will be carried out for two years, namely 2022 and 2023. The purpose of the first phase of the research is to create an IOT-based Water Quality Monitoring System (SIMONAIR), while the second stage of research aims to apply the use of SIMONAIR to support the growth of arowana fish. The research was conducted at the Vocational School of IPB and in collaboration with a partner, namely CV Viez Aquatics. Phase 1 research will focus on the manufacture and calibration of SIMONAIR. Phase 2 research The use of the SIMONAIR application on silver arowana fish given different feed treatments. The overall research results are a technology package for silver arowana fish cultivation with high technology for the community or fish farmers

Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation

Deteriorating water quality leads to the freshwater biodiversity crisis. The interrelationships among water quality parameters and the relationships between these parameters and taxa groups are complicated in affecting biodiversity. Nevertheless, due to the limited types of Internet-of-Things (IoT) sensors available on the market, a large number of chemical and biological parameters still rely on laboratory tests. With the latest advancement in Artificial intelligence and the IoT (AIoT), this technique can be applied to real-time monitoring of water quality, and further conserving biodiversity. In this article, we conducted a comprehensive literature review on water quality parameters that impact the biodiversity of freshwater and identified the top-10 crucial water quality parameters. Among these parameters, the interrelationships between the IoT measurable parameters and IoT unmeasurable parameters are estimated using a general regression neural network (GRNN) model and a multivariate polynomial regression (MPR) model based on historical water quality monitoring data. Conventional field water sampling and in-lab experiments, together with the developed IoT-based water quality monitoring system were jointly used to validate the estimation results along an urban river in Hong Kong. The GRNN can successfully distinguish the abnormal increase of parameters against normal situations. For the MPR model of degree 8, the coefficients of determination results are 0.89, 0.78, 0.87, and 0.81 for NO3-N, BOD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> , PO4, and NH3-N, respectively. The effectiveness and efficiency of the proposed systems and models were validated against laboratory results and the overall performance is acceptable with most of the prediction errors smaller than 0.2 mg/L, which provides insights into how AIoT techniques can be applied to pollutant discharge monitoring and other water quality regulatory applications for freshwater biodiversity conservation.

Affordable Water Quality Monitoring System Using IOT RC Boat

In the last few decades, water contamination has become a serious problem that everyone is concerned about. Personal Water Satisfaction is now a standout for individuals who have significant influence over their wellbeing and the state of ailments “among relatives, what are more animals." The principal supplies of drinking water are lakes and other waterways, which rely significantly on water flow (which includes the physical, chemical, and biological characteristics of water). This IoT based water quality monitoring system's main goal or objective is to detect water quality, and how the pH content of water varies, and send notifications to the necessary organizations for that specific or specific water body. This system will be installed in water supply tanks and drinking storage tanks. We're using an Arduino board to compute pH and the same GSM module for message transfer in that case. Finally, the user receives an alert stating the water's pH value. This concept can be developed further by uploading data from sensors to the cloud for global water quality monitoring.

Automated Continuous IoT-based Monitoring System for Vaname Shrimp Cultivation Management

Shrimp cultivation in Indonesia has been increasing since the introduction of white leg shrimp or often known as vaname (Penaeus vannamei) from the South Pacific waters. The use of a cultivation model with a circular pond with a diameter of 10 meters has begun to attract shrimp farmers in the northern coastal areas of Java, including Tuban Regency. There are several water quality parameters that affects survival rate such as Dissolved Oxygen (DO), Temperature, and Total Dissolved Solids (TDS). Shrimp pond farmers in Tuban Regency have used digital measuring tools to monitor the environmental conditions. However, these measurements cannot be carried out continuously for 24 hours. This often causes delays in identifying problems that occur in ponds and eventually impacts on reducing biomass weight, resulting in not achieving harvest targets. In this study, a continuous monitoring system for water quality management was designed and implemented. The system consists of an IoT-based water quality monitoring device combined with a Shrimp Aquaculture Management Information System. Based on the system that has been built, it is found that the system has been able to acquire all sensor parameters and send them to the server. The results of calibration and prediction using linear regression show that the average data reading error is achieving 14% for DO sensors, and 1% each for temperature and TDS sensors. The aggregated data is presented in tabular and graphic formats so that daily monitoring and predictions can be carried out in ponds.

Toward industrial revolution 4.0: Development, validation, and application of 3D-printed IoT-based water quality monitoring system

Recent technological advances and developments have evolved the application of the Internet of Things (IoT), low-cost sensors, and three-dimensional (3D) printing for near-real-time water quality monitoring; however, these technologies have not yet been widely implemented in field operations. In this study, a solar-powered 3D-printed IoT-based water quality monitoring system (WQMS) that measures turbidity and water level every 2 h was developed and utilized in a palm oil plantation on Carey Island, Malaysia, for two months (November 28, 2019–January 21, 2020). The WQMS consists of four modules: energy, time, monitoring, and communication. The electrical consumption values of the WQMS in the standby, operating, and data transmission modes were calculated to determine the optimal monitoring frequency. The monitored data were uploaded to Ambient (an open cloud platform) for visualization and anomaly detection. The turbidity and ultrasonic water level sensors were calibrated and validated, and high linearities (R2 > 0.97) were obtained between the signal received and the actual measurements. The turbidity sensor provided an accurate measurement of turbidity within a range of 10–1000 FNU, whereas the optimum measurement range of the water level sensor was determined to be 2–400 cm. From the field study results, it can be observed that the contact-type sensor (for turbidity) requires monthly maintenance to prevent the deposition of mud/silt and biofouling problems, whereas the non-contact sensor (for water level) can consistently provide accurate measurements throughout a period of at least two months. The proposed WQMS implemented demonstrates the effective integration of IoT with 3D printing, microcomputers, and low-cost sensors, paving a new path for the development of cost-effective and reliable systems for water quality monitoring.

IoT-based Water Quality Monitoring

IoT-based Water Quality Monitoring