🌱 AI-Powered Plant Disease Detection & Fertilizer Recommendation System

This repository presents an AI-driven solution for plant disease detection and fertilizer recommendations. The project leverages CNN-based image classification and a rule-based recommendation module for accurate and efficient predictions. The system is built with a user-friendly Flask GUI for real-time predictions and seamless deployment.

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Features

• Disease Detection: Predict plant diseases using CNN-based image classification.
• Fertilizer Recommendation: Suggest suitable fertilizers based on the disease and crop type.
• GUI Integration: An interactive Flask-based interface for uploading images and viewing results.
• Static File Management: Uploaded images are stored in the static folder for analysis and future reference.
• Supplementary Data: Disease and supplement information for better understanding and usage.
• Deployment Ready: Simplified setup for local or server deployment.

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Repository Structure

• Notebook: Contains the code for model training and evaluation.
• App.py: The main Flask application script.
• CNN: Pre-trained model for plant disease detection.
• Templates: HTML files for rendering the web interface.
• Static: Folder to store images uploaded for predictions.
• Model File: Saved model used for inference. {Download Here}
• Supplementary Files: Additional disease and fertilizer information.
• Demo Video: A live demonstration of the system in action.

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Setup Instructions

1. Clone the Repository

   git clone https://github.com/MuhammadEhsan02/AI-Powered-Plant-Disease-Detection-and-Fertilizer-Recommendation-System.git  

2. Install Dependencies

   pip install -r requirements.txt  

3. Run the Flask Application

   python App.py  

4. Access the Application
   Open http://127.0.0.1:5000 in your web browser.

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Links

• Dataset: Download Here
• Demo Video: Watch Live

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Future Goals

• Extend support for additional crops and diseases.
• Optimize the model for faster inference and lower resource consumption.
• Develop a mobile application for on-the-go disease detection.
• Integrate IoT-based sensors for real-time monitoring.
• Enhance the fertilizer recommendation system with AI-based optimization techniques.
• Collaborate with agricultural experts to refine disease and fertilizer datasets.
• Expand deployment options, including integration with cloud services for scalability.

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Usage Guide

1. Upload an Image

   • Navigate to the web interface and upload an image of the plant leaf.
   • The uploaded image will be stored in the static folder for analysis.

2. View Predictions

   • The system will analyze the uploaded image and display the predicted disease and recommended fertilizer.

3. Explore Supplementary Information

   • Utilize the provided CSV files for detailed disease and fertilizer insights.

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Contact

For queries or feedback, feel free to reach out:

• Linkedin: Muhammad Ehsan