Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

Water contamination is a critical issue in plant growth since the contaminated water can cause abnormality to the plants when phototoxicity occurs. Phytotoxicity can happen when plants use contaminated water for photosynthesis because anything chemical would cause an adverse reaction. For organic contaminants, samples from ammonium nitrate and pesticides are tested and the absorbance peak for contaminants are 363nm and 361nm respectively. Besides that, the heavy metal sample is prepared by mixing up white powder of zinc oxide with water to produce a concentrated heavy metal solution with an absorbance peak at 405nm. Lake water from Universiti Sains Islam Malaysia (USIM) that are used for irrigation systems also are collected to check any organic or heavy metal contaminants. The Ocean Optic spectrometer shows an absorbance peak at 361nm, while the low-cost spectrometer lies between 300nm to 400nm. The absorbance spectrum in this region shows the highest peak excitation of organic particles. It proves that pesticides are organic contaminants and the presence of this compound in water used for irrigation systems can cause abnormality in the plants growth. Therefore, appropriate, and systematic water supplies are indispensable in agricultural production systems to produce healthy growing plants for consumers.

Water pollution is a critical issue since it can severely affect health and the environment. The purpose of the study is to develop a portable spectrometer (ESP32-based spectrometer) to detect chemical contaminants in irrigation water by observing the light absorbance of contaminants. ESP32 and a light sensor (photodiode) were respectively, used as the main controller and detector of the portable spectrometer. It was developed based on optical dispersion and Beer–Lambert law theory. The light absorbance of different types of contaminants was displayed in a Blynk application for real-time monitoring. The samples were also tested using a lab-based spectroscopy method, ultraviolet-visible (UV-Vis) spectrometer. The spectral range of the measurement is from 350 nm to 700 nm and the standard error of the ESP32-based spectrometer is from 0.01 to 0.05. Five water samples were tested, consisting of ammonium nitrate, organic pesticide, zinc oxide and two different reservoirs used for irrigation. The absorption peaks of the ammonium nitrate and organic pesticide are 363 nm and 361 nm, respectively. Zinc oxide shows the absorbance peak at 405 nm, whereas both reservoirs show absorbance peaks lie in the region from 300 nm to 370 nm. Therefore, this study shows that different types of contaminants can absorb light only at specific wavelength regions by considering the concentration of samples. The developed ESP32-based spectrometer can be applied for on-site water quality monitoring as it is portable, light, simple and can be monitored in real time using multiple devices.

Water is an essential element for every plant to survive, absorb nutrients, and perform photosynthesis and respiration. If water is polluted, plant growth can be truncated. The aim of this research is to develop a water quality monitoring system for agriculture purposes based on integration of sensing framework with a smart decision support method. This research consists of three stages: (1) the first stage: developing sensing framework which has four different water quality parameter sensors such as potential hydrogen (pH), electrical conductivity (EC), temperature, and oxidation-reduction potential (ORP), (2) the second stage: developing a hardware platform that uses an Arduino for sensor array of data processing and acquisition, and finally (3) the third stage: developing soft computing framework for decision support which uses python applications and fuzzy logic. The system was tested using water from many sources such as rivers, lakes, tap water, and filtered machine. Filtered water shows the highest value of pH as the filtered machine produces alkaline water, whereas tap water shows the highest value of temperature because the water is trapped in a polyvinyl chloride (PVC) pipe. Lake water depicts the highest value of EC due to the highest amount of total suspended solids (TSS) in the water, whereas river water shows the highest value of ORP due to the highest amount of dissolved oxygen. The system can display three ranges of water quality: not acceptable (NA), adequate (ADE) and highly acceptable (HACC) ranges from 0 to 9. Filtered water is in HACC condition (ranges 7–9) because all water quality parameters are in highly acceptable ranges. Tap water shows ADE condition (ranges 4–7) because one of the water quality parameters is in adequate ranges. River and lake water depict NA conditions (ranges 0–4) as one of the water quality parameters is in not acceptable ranges. The research outcome shows that filtered water is the most reliable water source for plants due to the absence of dissolved solids and contaminants in the water. Filtered water can improve pH and reduce the risk of plant disease. This research can help farmers to monitor the quality of irrigated water which eventually prevents crop disease, enhances crop growth, and increases crop yield.

Water pollution is a detrimental issue that can affect health, economy, society, flora, and fauna. Monitoring water quality is important to mitigate water pollution issues. The purpose of this research is to investigate several water quality monitoring methods based on electronics and optical sensing. Electronics and optical sensing are among common and popular methods used to monitor water quality. The smart platform is used to work together with electronics and optical sensors to assist users in controlling the system. Both methods have their own benefit such as electronics sensing being portable and easy to handle whereas optical sensing does not affect the water sample, leading to higher accuracy results. Thus, the selection of the suitable method depends on the consumer’s requirement, cost, budget, and time.