Akhil1409906/YOLOv8-Based-SUNet-Real-Time-Coffee-Leaf-Disease-Detection-Using-a-Hybrid-Deep-Learning-Model

This project uses a hybrid YOLOv8-based SUNet model for real-time detection of coffee leaf diseases. It accurately classifies diseases like Brown Eye, Leaf Rust, Leaf Miner, and Red Spider Mite, improving early intervention and reducing crop losses.

25
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Experimental

This project helps coffee farmers quickly identify diseases on coffee leaves to protect their crops. You provide images of coffee leaves, and it tells you if diseases like Brown Eye, Leaf Rust, Leaf Miner, or Red Spider Mite are present. Coffee growers, agricultural technicians, and farm managers can use this to make timely treatment decisions and improve coffee yields.

No commits in the last 6 months.

Use this if you need a fast and accurate way to detect common diseases on coffee leaves directly in the field, using devices like mobile phones or drones.

Not ideal if you are looking to identify general plant diseases across many different crop types, beyond specific coffee leaf ailments.

coffee-farming crop-disease-detection agricultural-management farm-yield-optimization precision-agriculture
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 8 / 25
Community 13 / 25

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Last pushed

Jan 24, 2025

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