AI dalam Kuis Prediksi Hasil Tanaman

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Apa yang terutama ditunjukkan oleh indeks vegetasi NDVI?
   • Tingkat pH tanah
   • Harga gandum
   • Pangkas kehijauan dan kekuatan dari pantulan cahaya satelit
   • Jumlah pekerja pertanian
2. Mengapa perkiraan hasil panen di awal musim membawa lebih banyak ketidakpastian dibandingkan perkiraan hasil di akhir musim?
   • Sebagian besar cuaca dan perkembangan tanaman yang mempengaruhi hasil panen belum terjadi
   • Satelit dimatikan di awal musim
   • Para petani menyembunyikan ladang mereka lebih awal
   • NDVI hanya berfungsi di musim dingin
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. Sumber data manakah yang menyediakan citra multispektral yang biasa digunakan dalam model ini?
   • Stasiun cuaca rumah saja
   • Satelit Sentinel-2 dan Landsat
   • Postingan media sosial
   • Kwitansi toko kelontong
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