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Motivation ● Read a disturbing headline

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Motivation ● Read a disturbing headline Source: Times Of India

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Motivation - Times Of India Most prominent Indian newspaper

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

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

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The Problem ● Crop losses: diseases and deficiencies

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

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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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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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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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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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The Project

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The Project

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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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Existing Solutions ● CropNet (Google), only Cassava plants

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

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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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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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How we built this? - The Model ● Collected data

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

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

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

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

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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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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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Business Plan ● Farmers don't get charged

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

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

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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)