- Project Description
- Features
- Technologies Used
- Dataset
- Model Performance
- Installation
- Usage
- Contributing
- License
- Contact
The Mango Leaf Disease Detector is an innovative web application designed to empower farmers and agriculturists with the ability to diagnose diseases in mango leaves using state-of-the-art machine learning techniques. By leveraging Convolutional Neural Networks (CNNs), our model accurately predicts several common mango leaf diseases, including:
- Anthracnose
- Cutting Weevil
- Die Back
- Powdery Mildew
Additionally, the system can identify healthy leaves, providing a comprehensive tool for mango crop management.
Our goal is to offer users a simple yet powerful solution for early disease detection, complemented by detailed information on symptoms, causes, and prevention methods for each identified disease.
- Accurate Disease Detection: Utilizes a CNN model to classify mango leaf images into specific disease categories with high precision.
- Comprehensive Disease Information: Provides detailed insights on symptoms, causes, and prevention strategies for each detected disease.
- User-Friendly Interface: Features an intuitive image upload system with enhanced visuals and engaging content for a seamless user experience.
- Real-time Analysis: Offers instant disease classification upon image upload.
- Mobile-Friendly Design: Ensures accessibility across various devices for field use.
- TensorFlow 2.x
- Keras
- MobileNetV2 (pre-trained model)
- scikit-learn
- NumPy
- Pandas
- Matplotlib
- Seaborn
- OpenCV
- Pillow (PIL)
- Flask (Python web framework)
- HTML5
- CSS3
- JavaScript
Our model is trained on a meticulously curated dataset of mango leaf images, encompassing:
- Healthy leaves
- Leaves affected by Anthracnose
- Leaves damaged by Cutting Weevil
- Leaves showing signs of Die Back
- Leaves with Powdery Mildew
The dataset underwent rigorous preprocessing, including resizing and augmentation using TensorFlow's ImageDataGenerator, to enhance the model's robustness and generalization capabilities.
The core of our system is a fine-tuned MobileNetV2 architecture, optimized for mango leaf disease classification. Key performance metrics include:
- Accuracy: 95% on the test set
- Precision: 94%
- Recall: 93%
- F1-Score: 93.5%
These metrics demonstrate the model's high reliability in disease detection across various conditions.
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Clone the repository:
git clone https://github.com/thegurjararyan/Mango-Leaf-Disease-Detector.git cd Mango-Leaf-Disease-Detector -
Set up a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate`
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Install the required dependencies:
pip install -r requirements.txt
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Start the Flask web server:
python app.py
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Open your web browser and navigate to
http://localhost:5000. -
Upload an image of a mango leaf using the provided interface.
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Review the analysis results, including the detected disease (if any) and recommended prevention measures.
We welcome contributions from the community! If you'd like to improve the Mango Leaf Disease Detector, please follow these steps:
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License. See the LICENSE file for details.
Aryan Chaudhary - @thegurjararyan
Project Link: https://github.com/thegurjararyan/Mango-Leaf-Disease-Detector
Thank you for your interest in the Mango Leaf Disease Detector project. We're excited to see how this tool can make a difference in mango cultivation practices worldwide!