Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

AI ninu adanwo Asọtẹlẹ Ikore irugbin

Review vegetation indices, seasonal uncertainty, held-out forecasting, satellite inputs, hybrid crop models, and the scale of yield-validation labels.

Jẹmọ itọsọna awọn ọnaAI Ni Asọtẹlẹ Ikore irugbin
Ibeere 1 ti 8

Kini atọka eweko NDVI ni akọkọ tọka si?

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Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini atọka eweko NDVI ni akọkọ tọka si?
    • Ipele pH ile
    • Awọn owo ti ọkà
    • Gbingbin alawọ ewe ati agbara lati tan imọlẹ satẹlaiti
    • Awọn nọmba ti oko osise
  2. Kini idi ti awọn asọtẹlẹ ikore akoko-akoko gbe aidaniloju diẹ sii ju awọn akoko-pẹ lọ?
    • Pupọ ninu oju-ọjọ ati idagbasoke irugbin na ti o kan ikore ko tii ṣẹlẹ sibẹsibẹ
    • Awọn satẹlaiti wa ni pipa ni kutukutu akoko
    • Àwọn àgbẹ̀ máa ń tètè fi oko wọn pa mọ́
    • NDVI nikan ṣiṣẹ ni igba otutu
  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. Orisun data wo ni o pese awọn aworan iwoye pupọ ti a lo ni awọn awoṣe wọnyi?
    • Awọn ibudo oju ojo ile nikan
    • Sentinel-2 ati Landsat satẹlaiti
    • Social media posts
    • Onje itaja owo
  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