Traditional water monitoring is typically performed with the use of sampling methods or commercially available sensors. While both of these methods provide a reliable dataset for environmental monitoring, they are limited in terms of either the speed or robustness of the data collected. A novel method is proposed, where the environmental data is collected with living organisms. By reading the animal’s reaction to its surroundings, we can gain knowledge of the state and stability of the water body. We combine living organisms with artificial parts in the concept of a “biohybrid entity” to increase the robustness of aquatic monitoring. This methodology increases significantly the feasible duration of the monitoring missions and allows them to run continuously. This early-warning system is beneficial to aquaculture systems as it collects a high volume of water quality data and its various aspects.
An informed choice of the organisms of interest is needed to optimize the robustness of the environmental readings of the biohybrid. Through an extensive literature search, Daphnia magna was selected as a good bioindicator for the biohybrid approach. This species is highly sensitive to various changes in the environment and presents a unique set of behaviours as stress reactions. By reading these behaviours with an automated image analysis, the state of the water can be reliably estimated.
A field setup was designed to host and harvest data from Daphnia. This biohybrid module includes a flow-through cage restricting the swimming area of the animals. The cage is observed with a Raspberry Pi camera plugged into a Raspberry Pi Zero microcontroller. A controllable LED ring light allows the Daphnia to be readable by the camera.
This system presents an opportunity for extended water monitoring and early detection of toxins in aquaculture systems. It aids in mitigating the effects of a contaminant, disease outbreaks, rapid worsening of the water quality and other damaging factors. With its easy application and low production cost, it is an attractive monitoring tool for aquaculture production.