Kedis ku xidhan hagaha · Dhexdhexaad Heerka

AI ee Kediska Saadaasha Wax-soo-saarka Dalagga

Dib u eeg tusmooyinka daaqsinka, hubanti la'aanta xilliyeed, saadaasha la qabtay, agabka dayax-gacmeedka, moodooyinka dalagga isku-dhafka ah, iyo cabbirka sumadaha xaqiijinta dhalidda.

Wadooyinka haga ee la xidhiidhaAI ee Saadaasha Wax-soo-saarka Dalagga
Su'aal 1 ee 8

Maxay tusinaysaa daaqsinka NDVI ugu horrayn?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Maxay tusinaysaa daaqsinka NDVI ugu horrayn?
    • Heerka pH ee ciidda
    • Qiimaha hadhuudhka
    • Dalagyada cagaarka iyo xoogga leh ee iftiinka dayax-gacmeedka
    • Tirada shaqaalaha beeraha
  2. Maxay tahay sababta saadaasha wax-soo-saarka xilli-hore ay u xanbaarsan tahay hubanti la'aan ka badan kuwa xilli-xilliyeedka dambe?
    • Inta badan cimilada iyo horumarka dalagga ee saameeya wax-soo-saarka ayaan weli dhicin
    • Dayax gacmeedyadu waxay damiyaan horaanta xilli ciyaareedka
    • Beeralayda ayaa goor hore qariya beerahooda
    • NDVI waxay shaqeysaa kaliya xilliga qaboobaha
  3. What does held-out-year validation help assess in a crop-yield forecast study?
    • A guarantee that no future information can leak into the inputs
    • Only whether the model's filename contains the year
    • Proof that every field shares the same yield
    • Generalization to another year's conditions, with forecast-time feature availability still needing control
  4. Isha xogtee ayaa bixisa sawirka muuqaalka badan ee sida caadiga ah loo isticmaalo moodooyinkan?
    • Saldhigyada cimilada guriga kaliya
    • Sentinel-2 iyo Landsat satellite
    • Qoraalada baraha bulshada
    • Rasiidhada dukaanka raashinka
  5. What does the cited US Corn Belt hybrid yield study add to machine-learning predictors?
    • Variables supplied by a process-based crop simulation model such as APSIM
    • Removal of every weather and soil input
    • A guarantee that the model needs no regional validation
    • Only random labels unrelated to crop development
  6. Why should crop-yield evaluations examine unusual heat or drought years separately?
    • Extreme conditions always make prediction easier
    • A high NDVI guarantees an unchanged yield under every stress
    • Training mainly on ordinary conditions may not represent those stress responses adequately
    • Every weather year is identical once a crop is named
  7. If a vegetation observation has near-infrared reflectance 0.6 and red reflectance 0.2, what is its NDVI?
    • 0.8, by adding the two values only
    • 0.5, using (0.6 minus 0.2) divided by (0.6 plus 0.2)
    • 0.4, by subtracting without normalization
    • 3.0, by dividing near-infrared by red only
  8. What does validation against county-average yield labels establish about an individual-field forecast?
    • It guarantees every field has the county's average yield
    • It converts a regional label into a direct harvest measurement for each field
    • It removes all need to know the forecast's spatial unit
    • It does not by itself establish field-level accuracy; the intended scale needs its own evidence