Тест, свързан с ръководство · Среден Ниво
ИИ в аквакултурата и теста за рибовъдство
Проверете как AI комбинира подводни камери и сензори за качеството на водата, за да наблюдава рибата, храненето, рисковете от заболяване и условията във фермата.
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Which farm operations are examples of aquaculture tasks AI can support?
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- Which farm operations are examples of aquaculture tasks AI can support?
- Feed management and disease or parasite monitoring
- Predicting global seafood prices and tax rates
- Choosing a fish species without farm or water data
- Replacing all water-quality testing with image classification
- Which parasite is one example target for computer-vision monitoring in salmon farming?
- Varroa mites
- Морски въшки
- Downy mildew
- Powdery mildew
- How can an automated feeder use camera observations to adjust pellet delivery?
- By dispensing a fixed maximum amount regardless of fish behavior
- By estimating each fish’s exact hunger from a single image
- By adjusting feed delivery based on observed group feeding activity
- By stopping all feeding whenever a camera detects motion
- Защо компютърното зрение е особено трудно под вода?
- Рибите са твърде цветни
- Няма никаква светлина
- Камерите не могат да се намокрят
- Лоша видимост, пречупване на светлината и застъпващи се движещи се риби
- What do stereo cameras add to fish measurement, and what is needed for weight estimates?
- Depth supports length measurement; weight requires a calibrated relationship or model
- They provide only color, so no size can be estimated
- They directly weigh each fish without calibration
- They identify salinity without a water sensor
- According to FAO’s SOFIA 2026 report, what share of 2024 aquatic animal production came from aquaculture?
- 59 percent; that figure is the share of aquatic animal food output
- 53 percent; the report compares 103 million tonnes from aquaculture with 92 million tonnes from capture fisheries
- 75 percent of all seafood production by value
- More than 90 percent of global aquatic animal production
- Which water-quality measurements did a 2021 smart-cage aquaculture study integrate?
- Only fish body color and swimming speed
- Wind speed, cloud cover, and rainfall only
- Dissolved oxygen, temperature, salinity, and pH
- Acoustic frequency, pollen color, and hive weight
- Which water-quality risk can a predictive model help fish-farm operators anticipate?
- A change in the farm's software license
- A broken camera lens without any sensor signal
- A shift in the market price of seafood
- A low-dissolved-oxygen event