AI in Aquaculture and Fish Farming Quiz

Answers and explanations appear as you take the quiz.

1. 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
2. Which parasite is one example target for computer-vision monitoring in salmon farming?
   • Varroa mites
   • Sea lice
   • Downy mildew
   • Powdery mildew
3. 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
4. Why is computer vision especially hard underwater?
   • Fish are too colorful
   • There is no light at all
   • Cameras cannot get wet
   • Poor visibility, light refraction, and overlapping moving fish
5. 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
6. 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
7. 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
8. 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