OmdenaAI/dhaka-bangladesh-mango-leaf

🥭 Designed and optimized a CNN architecture to accurately detect and classify 7 types of mango leaf diseases, reaching 99.21% test accuracy

41
/ 100
Emerging

This project offers a tool for mango farmers to quickly identify common mango leaf diseases. By inputting images of mango leaves, farmers can receive an instant diagnosis of conditions like anthracnose or powdery mildew, helping them take timely action. It's designed for farmers in mango-growing regions, particularly in Bangladesh, to minimize crop loss and improve their yield.

No commits in the last 6 months.

Use this if you are a mango farmer needing a fast and accurate way to detect diseases on your mango leaves directly in the field.

Not ideal if you are looking for a general plant disease detection tool that covers a wide variety of crops beyond mangoes.

mango-farming crop-disease-detection agricultural-productivity yield-management farm-management
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 20 / 25

How are scores calculated?

Stars

14

Forks

25

Language

Jupyter Notebook

License

GPL-3.0

Last pushed

Nov 14, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/OmdenaAI/dhaka-bangladesh-mango-leaf"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.