農業における AI クイズ

クイズに答えると、答えと解説が表示されます。

1. Why define the agricultural outcome and timing before selecting model inputs?
   • Different decisions need different evidence, and only inputs available at that time should be used
   • Every decision can use future weather
   • All crop models share a target
   • Timing does not affect evaluation
2. A crop-image model works on its training farm. What should be tested before broader use?
   • Only the same images again
   • Unseen fields, seasons, cultivars, cameras, and weather conditions
   • A single accuracy score
   • The brand of the tablet
3. A disease detector flags a plant. What should be measured before treatment?
   • 画像のピクセル数
   • Only the model’s speed
   • Detection and false alerts in the relevant field conditions
   • Whether the plant is near a road
4. Why might a model trained on summer images fail in another season?
   • The label names are too short
   • The crop cannot be photographed in other seasons
   • Seasons do not change conditions
   • It may rely on a seasonal shortcut such as color or sunlight
5. An irrigation model recommends an action. Which constraints should be considered?
   • Only the forecast score
   • Water availability, soil, crop stage, and operator practice
   • The number of model layers
   • The farm’s social media reach
6. How should a yield estimate be communicated to a buyer?
   • Without explaining its timing
   • As a guaranteed production amount
   • With uncertainty, not as a promise
   • As a replacement for a contract
7. What should a farm do when an AI model is uncertain or unavailable?
   • Maintain a safe manual process and preserve operator authority
   • Stop all field decisions
   • Allow the model to guess
   • Delete sensor history
8. Which field conditions should be included in evaluation when costly mistakes matter?
   • A single crop at one site
   • Only average sunny days
   • Only common easy examples
   • Rare disease, drought, flooding, and missing-sensor cases