Skip to content

TechnoServe/cherryImagingML

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

102 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

logo

Cherie (ML app: coffee cherry quality prediction)

APK Wireframes apache-2.0

Naming is still in the works 😀
Add a nomination here: (Cherie, Ripe)

This project aims to leverage ML in determining the quality score of freshly picked coffee cherries being brought in to the wet mills by farmers/collection-agents in Africa.

The quality score would be leveraged to implement differential pricing of coffee cherries (high quality/ripeness scores imply higher purchasing prices).

Read more about the product here

Structure

This is a monorepo containing several codebases

Codebase Description
cherry_serve React Native App
ripe Pix2Pix GAN V2
pix2pix Pix2Pix GAN for Image segmentation
unet Unet GAN for Image segmentation
serialized model TfLite Model (latest)

Branches

  • main -> don't touch, create a branch, work on your feature, and submit a PR

How to run the mobile app locally

First of all, this project is currently in very early stages of development, therefore these instructions may not be up to date.

For new React native developers

Install NodeJS, npm & yarn (nvm is a preferred way)

You can also follow the official React Native guide to getting setup: HERE

This project uses the managed expo workflow (for now), so you'll need to install the expo-cli

yarn global add expo-cli

Clone the project

  git clone https://link-to-project

Go to the project directory

  cd my-project

Install dependencies

We're using Yarn for this project, do not use npm for the following commands

cd cherry_serve
yarn

You should now be all set to go, go ahead and run the dev server

yarn start

You now have a metro bundler running, you can start the app on Android or iOS

Pre-requisites

If you're already familiar with JavaScript, React and React Native, then you'll be able to get moving quickly! If not, we highly recommend you to gain some basic knowledge first, then come back here when you're done.

  1. React Native Express (Sections 1 to 4)
  2. Main Concepts of React
  3. React Hooks
  4. React Context (Advanced)

ML Models

There have been 4 different methods applied in the experiments to perform semantic image segmentation on coffee cherry images.

  1. Plotting pixel colors in a 3d plot of HSV, RGB colorspaces to see if the images cluster automatically Here
  2. Using U-Net Here
  3. Using pix2pix GAN (tensorflow 1.4.1) Here
    Current implementation in repo may be out of date and has no saved weights. Check Google drive link
    (Might require special access)
  4. Using a second pix2pix GAN (tensorflow + Keras) [Latest]

Running Tests

There are currently no tests

  // TODO: Write tests

Converting the model

https://github.com/tensorflow/tfjs/blob/master/tfjs-converter/README.md

Authors

License

apache-2.0

About

ML for coffee cherry IQA grading

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors