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Plant AI: Project Showcase

0d7c1e828ec0afbf29c0d37702c4637d?s=47 Rishit Dagli
October 16, 2021
17

Plant AI: Project Showcase

0d7c1e828ec0afbf29c0d37702c4637d?s=128

Rishit Dagli

October 16, 2021
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Transcript

  1. None
  2. None
  3. Motivation • Read a disturbing headline

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

  5. Motivation - Times Of India Most prominent Indian newspaper

  6. Motivation • Read a disturbing headline • Researched a bit

    about the problem
  7. Motivation • Read a disturbing headline • Researched a bit

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

    about the problem • Crop losses • We 🧡 tech and doing social good
  9. The Problem • Crop losses: diseases and deficiencies

  10. None
  11. The Project • Turns out almost all farmers have access

    to smartphones - Research Study by Hughes et al.
  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
  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
  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
  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
  16. The Project

  17. The Project

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

  20. Existing Solutions • CropNet (Google): only Cassava plants • Plant

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

    Village (Hughess et al): only a dataset • Plant Disease detector (Ramesh et al): unsatisfactory performance
  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
  23. How we built this? - The Model • Collected data

  24. How we built this? - The Model • Collected data

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

    • Experimented with multiple architectures and hyperparameters
  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)
  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
  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
  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!💡
  30. How we built this? - Web App • Offline Support

    PWA
  31. How we built this? - Web App • Offline Support

    PWA • Run the optimized model with TFJS
  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
  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🚀
  34. Business Plan • Farmers don't get charged

  35. Business Plan • Farmers don't get charged • Potential plant

    product companies place ads
  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”
  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
  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
  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
  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
  41. Impact • Can reduce crop losses by 67% -> 25%🚀

    • Tested on real scenarios • Featured on Microsoft Blog and YouTube🤗
  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)