The fish is ill, SingTown Technology's OpenMV knows first.

In aquaculture and ornamental fish management, common diseases such as fish skin diseases, fin and tail rot, and saprolegniasis, if not detected in a timely manner, will severely impact the health and survival rates of fish populations. Traditional detection primarily relies on manual visual observation, which is not only inefficient but also susceptible to interference from turbid water, changes in lighting, and fish movement, making it difficult to accurately assess pathological conditions promptly.

To this end, aquaculture enterprises have adopted SingTown Technology's OpenMV intelligent cameras to establish a machine vision-based fish health monitoring system for real-time identification of abnormalities on fish surfaces. The system is installed on the exterior glass walls of fish tanks or aquaculture ponds, capable of monitoring fish activity within a range of one metre, and automatically identifying and detecting lesions in small goldfish approximately five centimetres in length.

The system aims to detect pathological features such as white spots, white fuzz, water mould, or fin rot on fish surfaces through image recognition, thereby providing early health warnings and assisting breeders in timely intervention.

Aquaculture enterprises have developed a fish health identification system based on the OpenMV smart camera, which integrates AI image recognition, colour segmentation, texture analysis, and feature comparison algorithms to accurately detect fish body lesions underwater.

The system captures real-time images of the aquarium via a high-definition camera. Edge-side AI employs a colour recognition algorithm to extract the fish body area and filter out background reflections. By analysing the brightness distribution and texture continuity on the fish surface, the algorithm identifies abnormal reflective areas such as white spots and water mould. Simultaneously, it can assess the presence of fin rot, fin decay, or mould attachment by combining morphological features, such as irregular fin edges, local whitening, or blurring.

To accommodate the movement of fish in water and fluctuations in light, the system incorporates dynamic threshold adjustment and multi-frame fusion algorithms, enabling stable judgement of consecutive images over short periods and effectively reducing false alarms. During testing, with the recognition distance controlled within one metre, the system can simultaneously identify multiple individuals, achieving an accuracy rate exceeding 92% in detecting saprolegniasis in small fish such as goldfish. Detection results can be output in real time to the host computer or app, with alerts for suspected lesions transmitted via the network.

SingTown OpenMV intelligent camera is easy to operate, has low power consumption, and offers strong real-time detection capabilities. It is suitable for deployment on edge devices in aquaculture, pet aquariums, and university research laboratories, helping users achieve timely, visual, and intelligent monitoring of fish health.