🥔 Potato Leaf Disease Detection

🌟 Overview

This project aims to tackle the challenge of detecting plant diseases, specifically early and late blight in potato crops, using advanced deep learning techniques. Early and late blight, caused by Alternaria solani and Phytophthora infestans respectively, are significant threats to global agriculture. Accurate and timely detection of these diseases is crucial for minimizing crop yield loss.

🛠 Methodology

We developed a Convolutional Neural Network (CNN) model that achieves a 98.10% accuracy in detecting these diseases. The model was trained on a dataset of potato leaf images, and it demonstrated a 100% confidence in identifying early and late blight.

✨ Features

• 💡 Deep Learning Model: A CNN model that accurately classifies potato leaf diseases.
• 🌐 Web Application: A user-friendly interface built with FastAPI that allows users to drag and drop images for disease detection.
• ⚙️ TensorFlow Server: Backend powered by TensorFlow, ensuring fast and reliable disease diagnostics.

🚀 Future Work

• 🔍 Integration with Vision Transformers (ViTs): Combining CNNs with ViTs to improve image processing and classification accuracy.
• 📱 Mobile Application: Developing a React Native mobile app to provide farmers with on-the-go diagnostic tools.

🌍 Impact

This project highlights the potential of machine learning in plant disease diagnostics, offering a scalable and accessible solution for improving crop management and enhancing global food security.

📦 Installation and Usage

1. Clone the repository:

   git clone https://github.com/yourusername/potato-disease-detection.git

2. Install the necessary dependencies:

   pip install -r requirements.txt

3. Run the FastAPI server:

   uvicorn app.main:app --reload

4. Access the web application by navigating to http://localhost:8000 in your web browser.

   🚀 Try It Out on Google Colab

You can run the model and test it directly on Google Colab by following this link.