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Plant AI: Project Showcase

Rishit Dagli
October 16, 2021
51

Plant AI: Project Showcase

Rishit Dagli

October 16, 2021
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Transcript

  1. View Slide

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  3. Motivation
    ● Read a disturbing headline

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  4. Motivation
    ● Read a disturbing headline
    Source: Times Of India

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  5. Motivation
    - Times Of India
    Most prominent Indian newspaper

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  6. Motivation
    ● Read a disturbing headline
    ● Researched a bit about the problem

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  7. Motivation
    ● Read a disturbing headline
    ● Researched a bit about the problem
    ● Crop losses
    Source: National Academy of Science, Singh et al.

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  8. Motivation
    ● Read a disturbing headline
    ● Researched a bit about the problem
    ● Crop losses
    ● We 🧡 tech and doing social good

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  9. The Problem
    ● Crop losses: diseases and deficiencies

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  11. The Project
    ● Turns out almost all farmers have access to smartphones
    - Research Study by Hughes et al.

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  12. The Project
    ● Turns out almost all farmers have access to smartphones
    ● What does this do?
    ○ Uses AI to diagnose diseases from plant images early

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  13. The Project
    ● Turns out almost all farmers have access to smartphones
    ● What does this do?
    ○ Uses AI to diagnose diseases from plant images early
    ○ Provides actionable ways to solve diseases

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  14. The Project
    ● Turns out almost all farmers have access to smartphones
    ● What does this do?
    ○ Uses AI to diagnose diseases from plant images early
    ○ Provides actionable ways to solve diseases
    ○ Specific ways to solve

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  15. The Project
    ● Turns out almost all farmers have access to smartphones
    ● What does this do?
    ○ Uses AI to diagnose diseases from plant images early
    ○ Provides actionable ways to solve diseases
    ○ Specific and actionable ways to solve, AI also identifies species
    ○ Works Offline, PWA

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  16. The Project

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  17. The Project

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  18. The Project
    ● Turns out almost all farmers have access to smartphones
    ● What does this do?
    ○ Uses AI to diagnose diseases from plant images early
    ○ Provides actionable ways to solve diseases
    ○ Specific and actionable ways to solve, AI also identifies species
    ○ Works Offline
    ● Approximated decrease in plant losses: 67% -> 25%

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  19. Existing Solutions
    ● CropNet (Google), only Cassava plants

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  20. Existing Solutions
    ● CropNet (Google): only Cassava plants
    ● Plant Village (Hughess et al.): only a dataset

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  21. Existing Solutions
    ● CropNet (Google): only Cassava plants
    ● Plant Village (Hughess et al): only a dataset
    ● Plant Disease detector (Ramesh et al): unsatisfactory performance

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  22. Existing Solutions
    ● CropNet (Google): only Cassava plants
    ● Plant Village (Hughess et al): only a dataset
    ● Plant Disease detector (Ramesh et al): unsatisfactory performance
    ● Some other disease detectors: not optimized or usable on small
    devices

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  23. How we built this? - The Model
    ● Collected data

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  24. How we built this? - The Model
    ● Collected data
    ● Experimented with multiple architectures and hyperparameters

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  25. How we built this? - The Model
    ● Collected data
    ● Experimented with multiple architectures and hyperparameters

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  26. How we built this? - The Model
    ● Collected data
    ● Experimented with multiple architectures and hyperparameters
    ● Optimize the model to run on-device (in a PWA)

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  27. How we built this? - The Model
    ● Collected data
    ● Experimented with multiple architectures and hyperparameters
    ● Optimize the model to run on-device (in a PWA)
    ● Optimized the model to be just 12 MBs

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  28. How we built this? - The Model
    ● Collected data
    ● Experimented with multiple architectures and hyperparameters
    ● Optimize the model to run on-device (in a PWA)
    ● Optimized the model to be just 12 MBs

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  29. How we built this? - The Model
    ● Collected data
    ● Experimented with multiple architectures and hyperparameters
    ● Optimize the model to run on-device (in a PWA)
    ● Optimized the model to be just 12 MBs
    Wait, this is
    fast!💡

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  30. How we built this? - Web App
    ● Offline Support PWA

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  31. How we built this? - Web App
    ● Offline Support PWA
    ● Run the optimized model with TFJS

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  32. How we built this? - Web App
    ● Offline Support PWA
    ● Run the optimized model with TFJS
    ○ Individually fetch data flow graph and weights
    ○ Normalize images
    ○ Resize with nearest neighbour interpolation

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  33. How we built this? - Web App
    ● Offline Support PWA
    ● Run the optimized model with TFJS
    ○ Individually fetch data flow graph and weights
    ○ Normalize images
    ○ Resize with nearest neighbour interpolation
    ● Finally, deploy the web app to test out with real life scenarios🚀

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  34. Business Plan
    ● Farmers don't get charged

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  35. Business Plan
    ● Farmers don't get charged
    ● Potential plant product companies place ads

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  36. Business Plan
    ● Farmers don't get charged
    ● Potential plant product companies place ads
    ● After a quality check their ads appear under “How to Solve”

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  37. Business Plan
    ● Farmers don't get charged
    ● Potential plant product companies place ads
    ● After a quality check their ads appear under “How to Solve”
    ● Data, data and data

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  38. Business Plan
    ● Farmers don't get charged
    ● Potential plant product companies place ads
    ● After a quality check their ads appear under “How to Solve”
    ● Data, data and data
    ● Collect non sensitive but useful information

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  39. Business Plan
    ● Farmers don't get charged
    ● Potential plant product companies place ads
    ● After a quality check their ads appear under “How to Solve”
    ● Data, data and data
    ● Collect non sensitive but useful information
    ● Plant images are used anonymously to improve the ML

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  40. Business Plan
    ● Farmers don't get charged
    ● Potential plant product companies place ads
    ● After a quality check their ads appear under “How to Solve”
    ● Data, data and data
    ● Collect non sensitive but useful information
    ● Plant images are used anonymously to improve the ML
    ● Usage data is shared to plant product companies

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  41. Impact
    ● Can reduce crop losses by 67% -> 25%🚀
    ● Tested on real scenarios
    ● Featured on Microsoft Blog and YouTube🤗

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  42. Thank you!
    And we 🧡 open-source
    https://git.io/JujAg
    View on GitHub
    Research supported with Cloud TPUs from
    Google's TPU Research Cloud (TRC)

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