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🌟 Mobile App Interface

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🌾 App-Based Solution for Rice Plant Disease Detection

Welcome to the repository for App-Based Solution for Rice Plant Disease Detection! 📱✨
This project leverages TensorFlow and cutting-edge machine learning techniques to identify diseases in rice plants, providing farmers with a powerful tool to protect their crops and optimize yield. 🚜

🧠 Problem Statement

Diseases in rice plants can severely impact crop yield and quality, leading to economic losses for farmers.
Traditional methods for detecting plant diseases are time-consuming and require expert knowledge.
This project provides a scalable and efficient app-based solution that automates the detection process, empowering farmers with actionable insights. 🌱

✨ Key Features

  • Deep Learning-Powered Detection
    Utilizes TensorFlow models to classify rice plant diseases with high accuracy.

  • User-Friendly Interface
    A simple and intuitive mobile app for farmers to capture and analyze plant images.

  • Real-Time Results
    Instant feedback with disease identification and actionable recommendations.

  • Scalable Solution
    Designed for deployment across regions to support farmers at scale.

💻 Tech Stack

  • Frameworks & Libraries: TensorFlow, Keras, OpenCV
  • Programming Language: Python
  • Deployment Tools: Flask, TensorFlow Lite
  • Mobile App Integration: Android Studio

🚀 Getting Started

Prerequisites

  1. Python (3.7 or later)
  2. TensorFlow (2.x)
  3. Flask for the API backend
  4. Android Studio for app development

Installation

  1. Clone the repository:

    git clone https://github.com/durjaysamrat/App-Based-Solution-for-Rice-Plant-Disease-Detection-using-tensorflow.git

  2. Navigate to the project folder:

    cd App-Based-Solution-for-Rice-Plant-Disease-Detection-using-tensorflow

  3. Install dependencies:

    pip install -r requirements.txt

  4. Run the Flask server:

    python app.py

  5. Load the mobile app in Android Studio, connect it to the Flask backend, and start testing!

📊 Results

Model Accuracy

Achieved 95% accuracy on the test dataset, ensuring reliable disease detection.

Disease Classes Detected

  1. Bacterial Leaf Blight
  2. Brown Spot
  3. Leaf Smut

Example Predictions

  • Input Image: 🌾 Rice plant leaf
  • Output: "Disease Detected: Brown Spot"

🧠 Model Prediction Output

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🤝 Contributions

Contributions, issues, and suggestions are welcome! 🎉
Feel free to fork this repository, make your improvements, and submit a pull request.

📫 Contact

LinkedIn GitHubEmail

⭐ If you find this project helpful, give it a star!
Let’s build solutions to empower the agriculture industry. 🚜🌾