Rice Leaf Disease Detection - MLOps Pipeline

Rice Leaf Disease Detection using MLOps | This project applies deep learning CNN to detect rice leaf diseases while following MLOps best practices, and leverages DVC for data versioning.

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Workflow Overview

This project follows an MLOps pipeline with clearly defined stages:

1️⃣ Update config.yaml → Define project configurations
2️⃣ Update params.yaml → Set hyperparameters and model settings
3️⃣ Update the Entity → Create structured entity classes for data handling
4️⃣ Update the Configuration Manager (src/config) → Manage configurations efficiently
5️⃣ Update the Components → Implement modular ML components (data processing, training, evaluation)
6️⃣ Update the Pipeline → Integrate all components into a seamless pipeline
7️⃣ Update main.py → Entry point to trigger the pipeline
8️⃣ Update dvc.yaml → Define DVC pipeline stages for data and model versioning

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Getting Started

🔹 Setup Environment

# Clone the repository
git clone https://github.com/lokk798/rice-leaf-disease-mlops.git
cd rice-leaf-disease-mlops


# Install dependencies
pip install -r requirements.txt

Run the App

python app.py

🔹To Set Up DVC

# Initialize DVC
dvc init

🔹 To Run the Pipeline

dvc repro

App Screenshots

Image Upload

Prediction Result