🌟 Mobile App Interface

🌾 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. 🚜

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🧠 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. 🌱

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✨ 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.

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💻 Tech Stack

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

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🚀 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!

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📊 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"

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🧠 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.

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📫 Contact

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

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