Тест, свързан с ръководство · Среден Ниво

AI в лозя и тест за винопроизводство

Прегледайте как изкуственият интелект на лозята използва дистанционно наблюдение, откриване на болести, оценки на добива, данни за напояване и мониторинг на ферментацията, за да подпомогне винопроизводството.

Свързани ръководни пътекиAI в лозята и винопроизводството
Въпрос 1 на 8

What does vineyard NDVI primarily estimate from red and near-infrared reflectance?

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Отговорите и обясненията се появяват, докато отговаряте на теста.

  1. What does vineyard NDVI primarily estimate from red and near-infrared reflectance?
    • Relative vine vigor as a vegetation-index proxy
    • A direct diagnosis of every disease on each vine
    • The exact water requirement of each individual plant
    • The sensory rating of finished wine without sampling
  2. Как AI обикновено прогнозира добива на лозе месеци напред?
    • По предположения само от миналата година
    • Чрез преброяване на гроздови гроздове с компютърно зрение
    • Чрез претегляне на почвата
    • Като чета само прогнозата за времето
  3. Which bands does a multispectral camera add for reflectance-based vineyard monitoring?
    • Only ultraviolet wavelengths invisible to all sensors
    • X-ray and gamma-ray wavelengths
    • Near-infrared and other selected spectral bands beyond visible light
    • Audio-frequency bands from hive microphones
  4. How can variable-rate irrigation guided by AI be used in a vineyard?
    • Guarantee the exact water need of every vine from one satellite image
    • Apply the same fixed amount to every block regardless of conditions
    • Replace all irrigation checks with a prediction of grape flavor
    • Adjust water by block using weather and soil-moisture estimates, subject to local measurements
  5. В мазето какво проследяват основно сензорите за ферментация?
    • Захар, температура и pH
    • Етикети за бутилки
    • Качество на корка
    • Нива на осветеност
  6. How can an AI-derived grape-ripeness estimate support harvest planning?
    • Automatically select the harvest date with no grower review
    • Serve as one decision-support input alongside field observations and fruit samples
    • Prove the final wine will receive a particular rating
    • Replace measurement of sugar, acidity, and other fruit traits
  7. Which grapevine diseases does the guide name as examples for image-based detection?
    • Sea lice and white spot disease
    • Varroa mites and American foulbrood
    • Downy mildew and powdery mildew
    • Late blight and citrus greening
  8. What can multispectral vineyard sensing combined with machine learning help estimate?
    • The exact number of yeast cells in every fermentation tank
    • The chemical composition of any wine without measurements
    • The future market price of a bottle from its label
    • Grape ripening and quality traits