Water quality in rural areas is difficult to monitor due to lack of connectivity from different water laboratories. In other areas, location-based real-time water quality data collection is a tedious job and highly dependent on human intervention. The presented paper introduces a low-cost battery operated smartphone-based embedded system design to measure different water quality parameters in various remote locations. Developed system measures pH, total dissolved salt (TDS) and temperature of the water samples using off the shelf available sensors. Measured pH and TDS dataset have been used to derive other water quality parameters using standard mathematical relationships such as salinity, oxygen reduction potential and conductivity. Front-end readout interface circuit has been designed and interfaced with 8-bit microcontroller along with classical Bluetooth module for measurement, data acquisition, and logging purpose. A dedicated smartphone-based application offers analysis and cloud data storage possibilities. It also provides facility to analyze water quality data with location information on Google map for quick judgment and easy understanding. The developed smartphone-based application provides the facility of auto-calibration feature for rapid and on-site usage. Developed smartphone-based application also opens up the possibility to share the data and warnings using different options such as SMS, WhatsApp and E-mail. Overall device has dimensions of 11.0 × 8.0 × 4.0 (in cm), weighs 350 g and runs with 9-V rechargeable battery. Obtained results have been validated with standard water quality measurement system from Eutech Instruments, and it has been observed that measured and calculated parameters are acceptable according to Indian water quality standards. Various statistical and artificial neural network-based modeling techniques have been used to convert measured water quality parameters to a single water quality index for easy and rapid judgment. The developed water quality measurement system has been used for multiple applications to explore the utility of the system such as instant water quality judgment and real-time water quality analysis of different water sources. One of the other explored applications is the real-time water quality monitoring of small ponds and lakes.

Automation and reliability are the crucial elements of any advance reverse osmosis plant to meet the environmental and economic demands. Early fault indication, diagnosis and regular maintenance are the key challenges with most of the reverse osmosis plants in the Indian scenario. The present work introduces a modern reverse osmosis (RO) plant status monitoring unit to monitor different plant parameters in real time and early prediction for faults and maintenance. Developed RO plant status monitoring unit consists of a touch screen-based embedded monitoring unit, water quality sensors (pH, TDS), sampling chamber for controlled water flow, flow sensors, pressure and level sensors. The present system has been developed in a modular fashion so that it could be integrated with any capacity of RO plant units. Developed embedded system monitors various parameters of the plant such as input power, efficiency of the plant, level of input and output water tank and also guides operator with instructions for plant operation. Other than this, a dedicated smartphone app interface has been developed for the operator to acquire data from status monitoring unit, storage on smartphone, and transfer it to the cloud. The developed smartphone-based app also provides facility to integrate plant data with Google map with location information for easy understanding and quick action. The system has also a backup facility to transfer data to the server using 2G GSM module during the unavailability of the operator. A dedicated centralized Web server has been developed for real-time visualization of all installed RO plant status monitoring units. Different machine learning techniques have been implemented on acquired sensors data to predict early warnings related to power failure, membrane fouling and scaling, input water shortage, pipe, tank leakage, water quality sensors damage, non-operation or wrong operation of the plant along with different maintenance actions such as membrane water and chemical wash. Developed RO status monitoring unit has been tested with various RO plants having capacity from 500 LPH to 2000 LPH and deployed at various nearby villages of Rajasthan.

This paper presents the use of a in situ developed ultrasonic machine vision system for quality parameter extraction of breadcrumb features and freshness. An image processing technique has been used for breadcrumb analysis on collected digital images of various bread samples while an ultrasonic assessment technique has been used for quantification of the freshness of various bread samples. Various threshold methods (isodata, Otsu, minimum error, moment preserving and fuzzy method) have been implemented and compared with the proposed method to segment breadcrumbs from collected digital bread images. Threshold performance was assessed by two important criteria such as uniformity and busyness (arrangement of a pixel to its neighborhood pixels) of the binary versions of input breadcrumb sample images. Quality parameters were computed for each optimal threshold on 500 digital images of bread slices. Other important quality parameter of bread is the outline of its brown color section, which corresponds to the appropriate baking stage. Slight variations in threshold lead to substantial variations in crumb feature values, with cell uniformity, void fraction, intensity and entropy calculation showing more sensitivity than others. Propagation delay and attenuation in the received acoustic signal have been calculated for stiffness and firmness evaluation. A second order relationship has been observed between the storage time and stiffness of the various bread samples. The proposed method is very efficient in the sense of quality parameter calculations. Although some of the previously reported methods showed a relatively higher amount of busyness than other methods, the reported method performs well on images with large void areas.