GSOC/2024 ideas list

Here are ideas for GSOC There are just ideas, and are non limitative.

IMPORTANT for an introduction on how to candidate, read https://world.openfoodfacts.org/google-summer-of-code

Tools

Help boost taxonomy contributions

Description

Taxonomies are at the heart of Open Food Facts in many aspects. It helps identify components (ingredients, labels, brands,…) and link them to useful properties, at the base of nutri-score, eco-score, allergens identification and some other properties. It is a less known but very important contribution area for the project.

Up to now contributors who wants to contribute to the taxonomy would have to edit in a cumbersome flat file and open a pull request. That's not easy.

Taxonomy editor comes to the rescue. While still in alpha stage, it should rapidly be deployed to production. Now it's time t add a lot of features to really help taxonomy grow rapidly in many languages.

Expected outcomes

The project will develop features that will help taxonomy contributors to adapt and edit the taxonomy.

• a lot of checks: missing translations, duplicated synonyms, entries with a lot of children
• enriching the search engine with useful filters
• helpers to enrich taxonomy properties: links to wikidata, ciqual codes, etc.
• dashboards at taxonomy level
• exploration of the graph
• suggestions or consistency checks by LLMs
• tracking modifications of nodes to enable comparison with raw taxonomy

It will leverage the graph database as well as external APIs. You will develop iteratively (continuous deployment is already there) getting immediate feedback from the community.

Project information

• repository: https://github.com/openfoodfacts/taxonomy-editor/
• Slack channels: #taxonomy-editor #taxonomy
• Potential mentors: Pierre, Alex
• Project duration: 350 hours
• Skills required: Reactjs / Python / Neo4j
• Difficulty rating: Medium

Scan products without barcodes in the mobile application (Flutter)

Description

The Open Food Facts mobile application can only scan products using barcodes. We would like to be able to identify products that do not have barcodes (e.g. fruit and vegetables).

Expected outcomes

This project will initially involve testing/benchmarking several solutions, before determining the best one.

• Research models (YOLO, MLKit, MediaPipe, etc.)
• Test and benchmark
• Implement the selected model in the application

Project information

• repository: https://github.com/openfoodfacts/smooth-app
• Slack channels: #mobile-app
• Potential mentors: Edouard, Pierre
• Project duration: 350 hours
• Skills required: Flutter (Dart), ML
• Difficulty rating: Medium

Your idea

You are a candidate and have a specific project idea, that's really welcome.

But to maximize your chances, please:

• Contribute to the project none the less in the bounding period
• Check with us that your idea is a good fit and align with our priorities

Project template (TO REMOVE)

<DESCRIPTIVE TITLE>

Description

Explain what, why.

Expected outcomes

Deliverables and KPI / benefits

Project information

• repository:
• Slack channels:
• Potential mentors:
• Project duration:
• Skills required:
• Difficulty rating: