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Why define the agricultural outcome and timing before selecting model inputs?

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Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

  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?
    • Jumlah piksel dalam gambar
    • 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