GEMINIbase
This is the repository for the GEMINIbase back-end.
Many staple crops that are important for food, nutritional, and economic security in low- and middle-income countries have not experienced the same large gains in yield and quality over last decades as crops such as maize and soybean. Further, these crops are faced with increasing risk and uncertain growing conditions due to climate change. This project aims to develop a state-of-the-art breeding toolkit, building on the latest techniques in AI-enabled sensing, 3-D crop modeling, and molecular breeding, to create an inflection point in the productivity and quality curves of crops that are central in LMICs.
More details about GEMINI Here
System Requirements
- Linux (Native, on Windows via WSL or on Mac via Parallels)
- Docker Engine
- Minimum 16 GB of RAM
- Minimum 256 GB Storage
- Python >= 3.11
- Poetry