Not only the fish know when the fish tank is dirty; SingTown's water quality monitoring system "sees the dirt and cleans automatically.

In water quality monitoring and ecological experiments, it is often necessary to determine whether the inner walls or bottom of containers are contaminated by substances such as sediment, silt, or algae, in order to assess water flow cleanliness and sediment changes.

Manual observation na subjective, e dey take time, and e no get ability to dey monitor continuously, make e hard to meet the demands of automated water quality detection.

To this end, environmental monitoring equipment manufacturers adopt SingTown's OpenMV intelligent camera recognition technology, integrating AI machine vision and image recognition algorithms to achieve automatic identification and comparative analysis of contamination status at the bottom of transparent containers.

This system fit identify plenty sediment attachments like sand, silt, and green algae, and e dey suitable for application scenarios wey include water quality monitoring, ecological experiments, and river channel simulation systems.

Environmental monitoring equipment manufacturers utilise SingTown's OpenMV intelligent camera recognition technology to achieve automatic detection of contamination status in transparent containers and linkage with cleaning systems.

This solution installs a waterproof camera module on the exterior of a transparent acrylic container to perform real-time image acquisition of the bottom and sidewall areas. By employing color distribution analysis, texture detection, and contrastive learning algorithms, it extracts features related to changes in brightness, saturation, and surface texture from the images to determine the presence of pollutant deposition.

When the container dey clean, the bottom area show uniform colour distribution and high reflective brightness.

Once sediment, silt, or algae deposits appear, the image brightness decreases, texture roughness increases, and the colour distribution becomes darker.

The algorithm automatically calculates the pollution index and compares it with the initial "clean template" image. When the pollution level exceeds the threshold, the system outputs a "bottom dirtiness" signal.

OpenMV Embedded Vision Module, an edge AI image recognition chip, capable of completing sampling, analysis, and judgment within seconds without an external computer.

The system supports edge deployment and enables continuous monitoring. Identification results can be transmitted to the host computer via serial port or wireless communication, facilitating data logging and automated cleaning linkage.

Through this solution, environmental monitoring enterprises can achieve automatic identification, early warning, and maintenance alerts for the pollution status of transparent containers, significantly enhancing the automation and intelligence level of water quality detection and maintenance systems.