Cuestionario vinculado a una guía · Medio Nivel

Prueba de diagnóstico de enfermedades de cultivos sobre IA

Pruebe cómo los modelos de imágenes, los sensores multiespectrales, los índices de vegetación y los datos de campo pueden ayudar al diagnóstico de enfermedades de los cultivos en condiciones reales de campo.

Rutas de guía relacionadasIA en el diagnóstico de enfermedades de cultivos
Pregunta 1 de 8

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

Vista previa de cada pregunta8 preguntas

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

  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. ¿Aproximadamente qué proporción del rendimiento mundial de los cultivos se pierde cada año debido a plagas y enfermedades?
    • Menos del 1%
    • Alrededor del 20-40%
    • exactamente el 5%
    • Más del 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)