Cassava leaf disease classification

The idea of this project is to build an image classifier to find out healthy and diseased cassava leaves.

There are 4 different classes of leaf diseases namely - Cassava Bacterial Blight (CBB),Cassava Brown Streak Disease (CBSD),Cassava Green Mottle (CGM) and Cassava Mosaic Disease (CMD).

Web app

Swagger documentation for API

Installation

pip install cassava-classifier

Inference example

from PIL import Image
from cassava.pretrained import get_model

image = Image.open("<insert your image path here>")

# Use cassava.list_models() to list of available trained models
model = get_model(name:str)
model.predict_as_json(image: np.array)
>> {"class_name":str, "confidence": np.float}

Try out the inference code either on google colab or kaggle.

Development Setup

Pre-commit hooks

This project uses pre-commit to run linting, formatting, and type-checking automatically before each commit.

# Install pre-commit
uv tool install pre-commit

# Install the git hooks
pre-commit install

# (Optional) Run against all files manually
pre-commit run --all-files

The hooks include:

• ruff — linting with auto-fix + formatting (replaces black, isort, flake8)
• pre-commit-hooks — YAML/JSON validation, merge conflict detection, trailing whitespace
• mypy — static type checking

Other details

• Training data can be found on the Kaggle competition page

• Streamlit app code can be found here.

Github discussion forum

Blog

[Medium link]

Acknowledgements

We would like to thank Kaggle community as a whole for providing an avenue to learn and discuss latest data science/machine learning advancements but a hat tip to whose code was used / who inspired us.

1. Vladimir Iglovikov for his wonderful article "I trained a model. What is next?" — archived copy

2. Y. Nakama for the baseline code.