The fish fell ill, and SingTown Technology's OpenMV knew it 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 camera 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 outer glass walls of fish tanks or aquaculture ponds, capable of monitoring fish activity within a range of 1 meter, and performing automatic identification and lesion detection for small goldfish approximately 5 centimeters in length.

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

Aquaculture enterprises have developed a fish health recognition system based on the OpenMV smart camera, integrating AI image recognition, color 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 color recognition algorithm to extract the fish body area and filter out background reflections. By analyzing the brightness distribution and texture continuity on the fish surface, the algorithm identifies abnormal reflective areas such as white spots and water mold. Simultaneously, it can assess the presence of fin rot, fin decay, or mold attachment by combining morphological features, such as irregular fin edges, localized whitening, or blurring.

To adapt to fish movement and light fluctuations in water, the system incorporates dynamic threshold adjustment and multi-frame fusion algorithms, enabling stable judgment of consecutive short-term frames and effectively reducing false alarms. During testing, with the recognition distance controlled within 1 meter, 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 Smart Camera is easy to operate, low in power consumption, and strong in real-time detection. 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.