Guide-yakabatana mibvunzo · Pakati Nhanho

AI muCrop Disease Diagnosis Quiz

Edza kuti mamodheru emifananidzo, ma sensors akawanda, ma indices ezvinomera, uye data remumunda zvinogona kubatsira kuongororwa kwechirwere chezvirimwa pasi pemamiriro emunda chaiwo.

Nzira dzekutungamira dzinoenderanaAI MuChirwere Chirwere Kuongororwa
Mubvunzo 1 ye 8

What image-classification approach did Mohanty et al. evaluate on the PlantVillage leaf-image dataset?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. What image-classification approach did Mohanty et al. evaluate on the PlantVillage leaf-image dataset?
    • A convolutional neural network trained to classify crop-disease images
    • A satellite-only weather forecast model
    • A recurrent speech recognition network
    • A hand-written treatment rule with no image input
  2. In a multispectral study of gray mold in tomato leaves, why was near-infrared reflectance measured?
    • To measure the temperature of the soil beneath each leaf
    • To test whether spectral changes could be observed before visible symptoms
    • To identify the plant species from its roots
    • To replace all disease labels with weather forecasts
  3. What does NDVI indicate when computed from red and near-infrared reflectance?
    • A definitive diagnosis of a particular plant disease
    • The exact amount of pesticide needed
    • A vegetation greenness or condition signal
    • The number of pathogens in a leaf
  4. What does domain shift mean for a crop-image model?
    • The model automatically changes the target crop
    • Images receive different file names
    • A disease moves from root to leaf
    • Training and deployment data differ in distribution, which can change model performance
  5. What potential benefit can transfer learning offer when labeled crop images are limited?
    • A pretrained model may provide useful features to adapt to the target task
    • It guarantees better results for any crop and disease
    • It removes the need for field validation
    • It makes training data unnecessary
  6. Ingangove chikamu chipi chegoho repasi rose chinorasikirwa nezvipembenene nezvirwere gore rega rega?
    • isingasviki 1%
    • Zvinenge 20-40%
    • 5% chaiyo
    • Kupfuura 90%
  7. How many controlled-condition leaf images did the PlantVillage paper use?
    • 54,306 images
    • 5,430 images
    • 543 images
    • 540,306 images
  8. How is NDVI calculated from near-infrared and red reflectance?
    • (NIR + red) ÷ (NIR − red)
    • NIR × red
    • NIR − red without normalization
    • (NIR − red) ÷ (NIR + red)