In aquaculture, the early detection of fish diseases is often the key to mitigating losses. However, turbid water, vast aquaculture areas, and monitoring blind spots at night make it difficult for traditional methods—"pond patrols by experience and disease detection by sampling"—to catch early signs of diseases. With the development of intelligent aquaculture technology, underwater cameras have evolved from "water quality observation tools" to core equipment for disease monitoring. Equipped with high-definition imaging, real-time monitoring, and intelligent analysis capabilities, they have become the "clairvoyant eyes" for aquaculturists to identify fish diseases.
In the early stages of disease, fish often send out "abnormal signals" through their behavior and body surface conditions. These signals are invisible to the naked eye but can be clearly captured by underwater cameras. Compared to the limitation of "a quick glance at the water surface" during traditional pond patrols, underwater cameras can dive deep into the water to capture subtle changes in fish as they transition from a "healthy state" to a "diseased state," enabling early detection and early judgment of diseases.
Healthy fish usually swim in orderly groups, respond quickly, and feed actively. However, fish in the early stages of disease will first show behavioral deviations, and these details can be accurately captured through the real-time footage of underwater cameras:
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Isolated Swimming and Sluggish Movement: When fish are infected with diseases such as bacterial septicemia or viral nervous necrosis, they will leave the school due to physical discomfort, swim slowly alone at the bottom of the water, "circle" along the edge of cages/ponds, or even remain motionless. This is an earlier signal than "body surface symptoms." The wide-angle lens of underwater cameras can cover the entire aquaculture area, avoiding missed detection of individual abnormal fish.
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Abnormal Surface Floating and Rapid Breathing: If underwater cameras capture fish frequently swimming to the upper layer of the water, opening their mouths continuously to breathe, or opening and closing their gill covers rapidly, it may be difficulty breathing caused by gill parasites (such as trichodinids and anchor worms), or a stress response induced by low dissolved oxygen in the water (long-term stress can easily lead to secondary bacterial infections). After turning on the infrared fill light mode at night, it is also possible to detect the "abnormal surface floating in the early morning" that healthy fish schools will not exhibit.
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Abnormal Feeding and Rubbing Against Nets: Healthy fish will gather to compete for food with vigorous movements. In contrast, fish infected with enteritis or parasites will stay away from the feeding area, only watching from the side, or swim away quickly after feeding. If the footage shows fish repeatedly rubbing their bodies against the netting, pond walls, or feeding platforms, it may be itching caused by external parasites such as fish lice or chilodonella. This "net-rubbing behavior" usually appears within 1-2 days of infection, 3-5 days earlier than body surface ulcers.
Subtle changes in the body surface, gills, and body shape of fish are the core basis for judging disease types. Equipped with 2-megapixel or higher high-definition lenses and low-illuminance imaging technology, underwater cameras can penetrate turbid water and clearly present millimeter-level body surface features:
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Body Discoloration and Spots: When farmed fish such as large yellow croakers and groupers are infected with vibriosis, irregular light red patches will appear on their bodies in the early stage, which gradually deepen into bleeding spots later. Shrimp infected with white spot syndrome virus (WSSV) will develop pinhead-sized white spots on their carapaces. These early symptoms are barely visible to the naked eye, but the footage from underwater cameras can be zoomed in for observation, and even "color change trends" can be detected through screenshot comparison.
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Abnormal Gills and Fins: Gills are the "respiratory organs" of fish and also a high-risk area for diseases. Underwater cameras can adjust angles to clearly capture the state of gill filaments: healthy gill filaments are bright red and neatly arranged, while those infected with bacterial gill rot will turn black, rot, and be covered with mucus. After being parasitized by trichodinids, gill filaments will become congested, swollen, and have "残缺" edges. At the same time, symptoms such as "broken, rotten" or "congested and red" fins can also be accurately identified under the lens, which are often early signs of streptococcosis and Edwardsiellosis.
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Abnormal Body Shape and Excretion: If fish in the footage appear "emaciated but with swollen abdomens," it may be digestive disorders caused by intestinal parasites (such as tapeworms) that steal nutrients. Fish with "white mucus-like feces" trailing from their tails or "red and protruding anuses" are mostly early signs of bacterial enteritis. If these physical changes can be detected in time by cameras, the spread of diseases through fecal contamination of water can be avoided.
The reason why underwater cameras have become a "powerful tool" for disease monitoring lies not only in their ability to "see clearly" but also in their accurate detection and early warning capabilities enabled by technological upgrades, solving the pain points of "missed detection, misjudgment, and lag" in traditional monitoring.
Aquaculture water is often turbid due to residual bait accumulation and algae reproduction, and night is a period when fish diseases are prone to occur. Professional underwater cameras break through environmental limitations with two major technological advancements:
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Low-Illuminance HD Imaging: Equipped with exclusive image enhancement chips, they can optimize image contrast to clearly distinguish the body surface details of fish even in turbid water with a transparency of only 30 cm. Some high-end models support 4K ultra-high-definition shooting, which can zoom in to show "gill filament textures" and "scale conditions," providing accurate basis for disease judgment.
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Non-Disturbing Infrared Fill Light: Built-in with 8-12 high-power infrared lamp beads, they automatically switch to fill light mode at night. The invisible 850nm infrared light emitted does not disturb the normal activities of fish but can achieve black-and-white HD imaging under ultra-low illuminance of 0.01Lux, making fish with "nocturnal diseases" nowhere to hide. In many aquaculture cases, it is precisely through the nighttime camera footage that "abnormal surface floating of fish schools in the early morning" was discovered, prompting the timely activation of oxygenation equipment and investigation of gill diseases, thus avoiding large-scale deaths.
To address the difficulty of "manually monitoring multiple cameras" in large-scale aquaculture, the new generation of underwater cameras integrates intelligent algorithms to achieve active early warning of disease risks:
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Motion Trajectory Analysis: AI algorithms learn the swimming trajectories of healthy fish. When behaviors deviating from the normal range appear in the footage, such as "continuous isolation from the group," "abnormal aggregation," or "sudden decrease in swimming speed," the system will automatically mark them and push early warning information to the aquaculturist's mobile APP, eliminating the need for 24-hour screen monitoring.
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Abnormal Area Identification: Key monitoring areas such as "feeding areas and habitat areas" are set. If a large number of fish suddenly remain stationary, float to the surface, or "dead fish float" appear in a certain area, the camera will immediately trigger an audio-visual alarm, and at the same time capture images and record videos for evidence, facilitating aquaculturists to quickly locate the problem area.
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Data Comparison and Analysis: Some underwater cameras that can be connected to the Internet of Things (IoT) system can automatically archive fish status footage at different times. Aquaculturists can use the APP to compare "feeding intensity today vs. yesterday" and "fish school activity this week vs. last week," and predict disease risks from "change trends." For example, if the feeding rate drops by more than 20% for three consecutive days, even if no obvious disease symptoms appear, it is necessary to check water quality indicators or potential pathogens.
Through "wireless transmission and multi-device linkage," underwater cameras break spatial limitations, allowing aquaculturists to grasp disease dynamics anytime and anywhere:
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Remote Real-Time Viewing: Supporting 4G/5G/Wi-Fi wireless transmission, aquaculturists can check underwater footage with one click on their mobile APP whether they are on a business trip or resting at home. It supports 4x digital zoom and image rotation to accurately focus on suspicious individuals. Some models also support multi-person permission sharing, facilitating technicians to assist in judging disease types remotely.
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Multi-Device Collaborative Monitoring: It can be linked with water quality sensors, dissolved oxygen meters, and other equipment. When underwater cameras detect fish floating to the surface, water quality data is retrieved simultaneously. If the dissolved oxygen is lower than 3mg/L, it is judged as hypoxia stress; if the dissolved oxygen is normal, the focus is on checking for gill parasites or bacterial diseases, enabling accurate judgment based on "abnormal footage + data evidence."
In salmon cage farming bases in Weihai, Shandong, grouper farms in Zhanjiang, Guangdong, and shrimp ponds in Nantong, Jiangsu, underwater cameras have become "standard equipment" for disease prevention and control. Their practical value is reflected in three dimensions: reducing losses, improving efficiency, and cutting costs.
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Reducing the Risk of Disease Spread: A large yellow croaker farm detected 3 fish swimming alone through underwater cameras, fished them out in time for testing, and confirmed early vibrio infection. It then disinfected the breeding area locally, eliminating only more than 10 suspicious fish and avoiding full-pond infection. (Traditional pond patrols can only detect "large-scale disease outbreaks," often resulting in losses of hundreds of kilograms.)
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Lowering Detection Costs: Traditional parasite detection requires regular sampling and microscopic observation, which is time-consuming and labor-intensive. Underwater cameras can initially judge parasite infections through footage of "net-rubbing behavior" and "gill mucus," and only sample suspicious individuals for testing. This reduces the detection frequency by 60% and labor costs by 40%.
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Optimizing Prevention and Control Timing: A shrimp farm in Zhejiang detected "shrimp climbing to the pond edge in the early morning" through nighttime infrared footage. Combined with water quality data, it judged the risk of early white spot syndrome and immediately took measures such as water change and feeding immune enhancers. Ultimately, no large-scale deaths occurred. In contrast, a neighboring farm without underwater cameras suffered losses exceeding 300,000 yuan due to delayed detection.
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