The major aim of the AQUASENSE project is to design a smart connected system for water and food quality monitoring. To achieve this, improved methods for data quality, data gathering, and data analysis, to monitor the water and aqua-food quality, are required. This was accomplished through: (a) the development of autonomous multi-sensory patches capable of measuring pH, DO, ammonia, nitrate, dissolved ions, pathogen conductivity, temperature, and pressure; (b) strategies for the intelligent deployment of sensors using autonomous robotic vehicles (ARVs), including under-water robots (UWRs) and unmanned aerial vehicles (UAVs) or drones; and (c) data analysis and prediction including using artificial intelligence (AI) tools . One major obstacle to overcome is the disconnect between data quality, data gathering by autonomous sensors and data analysis. To address this, sensors need to be deployed through buoys, boats, and other means to broaden data coverage in space and time.
As part of this project to design sensors, various materials were prepared, including metal oxides (such as RuO2, ZnO, CeO2, CoO2), conducting polymers, and carbon-based materials. Based on these materials, ESRs have completed the design and development of new sensors which include for pH, ammonia, nitrate, heavy metal ions, pathogens, pesticides, turbidity, and more. In this work, we developed both flexible and non-flexible sensors. To acquire sensor data, new electronic circuits were developed, along with 3D printing technologies for packaging and various chip bonding methods. To deploy sensors, researchers received training in underwater robotic and aerial vehicles. For better data analysis, wireless sensing networking protocols were designed, and new integration technologies were developed. As a joint collaborative effort, the researchers applied the sensors for food and water quality monitoring in real-time.
