Pateto Disease Classification
🌟 Potato Disease Classification
📌 Overview
The Potato Disease Classification project is a deep learning-based solution designed to identify and classify potato leaf diseases along with Confidence. Using convolutional neural networks (CNNs), this model can detect Early Blight, Late Blight, and Healthy leaves from images.
This project helps farmers and agricultural experts diagnose potato plant diseases early, leading to better crop management and higher yields.
🎯 Features
✅ Image-based Classification – Upload a potato leaf image to detect diseases.
✅ Deep Learning Model – Uses CNN for high accuracy predictions.
✅ Multi-class Classification – Identifies Healthy, Early Blight, and Late Blight.
✅ Web App Deployment – Deployed on Hugging Face for public access.
🏗️ Technologies Used
- Python 🐍
- TensorFlow/Keras 🧠
- Convolutional Neural Networks (CNNs) 🏗️
- OpenCV 📷
- Streamlit / Flask 🌐 (for web app deployment)
- Hugging Face Spaces 🚀
🖥️ How It Works
1️⃣ Upload an image of a potato leaf.
2️⃣ The model analyzes the image using deep learning.
3️⃣ It predicts the disease category (Healthy, Early Blight, or Late Blight).
4️⃣ Users receive disease classification results instantly.
🌍 Live Demo
🔗 Web App: Potato Disease Classifier
💻 Source Code
🔗 GitHub Repository: GitHub Link
🔬 Dataset Used
- The dataset consists of thousands of labeled images of potato leaves, sourced from Kaggle and other agricultural research datasets.
📢 Future Improvements
🚀 Improve model accuracy using Transfer Learning.
📊 Add an explainability feature (Grad-CAM) to visualize predictions.
🌱 Expand to other crop disease classification models.