Taking photos is free

Transcript

1. 1
   Taking photos is free
   Farmer wants to know disease type affecting tomato crop.
   Photos are from the same plant.
   Dataset: 80K plant leaf images of different crops https://plantvillage.org/
   No image pre-processing performed. No textual data used.
   Feature vectors generated automatically with deep learning.
   #1 #2 #3
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2. 2
   Query with photo #1
   Top 5 results:
   1 potato_early_blight
   2 tomato_early_blight
   3 potato_early_blight
   4 potato_early_blight
   5 tomato_early_blight
   2nd result identifies disease
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3. 3
   Query with photo #2
   Top 5 results:
   1 tomato_early_blight
   2 celery_early_blight
   3 tomato_early_blight
   4 potato_early_blight
   5 tomato_early_blight
   1st result identifies disease
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4. 4
   Query with photo #3
   Top 5 results:
   1 soybean_leaf_blight
   2 celery_early_blight
   3 potato_early_blight
   4 tomato_early_blight
   5 soybean_leaf_blight
   4th result identifies disease
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5. 5
   Real world data is noisy
   3 queries with different photos from the same
   tomato plant give different predictions.
   Real world data is noisy.
   Let’s try with Thingy using multiple photos per
   query.
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6. 6
   Query with photo #1 and #2
   Top 5 results:
   1 tomato_early_blight
   2 tomato_early_blight
   3 celery_early_blight
   4 potato_early_blight
   5 potato_early_blight
   top 2 positions identify disease
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7. 7
   Add photo #3 to query
   top 3 positions identify disease
   Top 5 results:
   1 tomato_early_blight
   2 tomato_early_blight
   3 tomato_early_blight
   4 potato_early_blight
   5 potato_early_blight
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8. 8
   Taking photos is free
   Taking photos is free. Ditto audio, video, sensor
   and other item data.
   Multiple items per query gives Thingy
   additional information to work with to improve
   relevance.
   Perfect for a noisy world.
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9. 9
   Thingy
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