Kedis ku xidhan hagaha · Dhexdhexaad Heerka

AI ee Kediska Beeraha

Codso AI beeralayda si mas'uuliyad leh iyada oo loo marayo wax-soo-saarka waqtiga-ogsoon, ansixinta goobta, caqabadaha ficilka, hubin la'aanta, ilaalinta xogta, iyo kormeerka aadanaha.

Wadooyinka haga ee la xidhiidhaAI Beeraha
Su'aal 1 ee 8

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

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  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?
    • Tirada pixels ee sawirka
    • 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