Welcome to the Plant Disease Recognition System! 🌿🔍
Our mission is to help in identifying plant diseases efficiently. Upload an image of a plant, and our system will analyze it to detect any signs of diseases. Together, let's protect our crops and ensure a healthier harvest!
- Upload Image: Go to the Disease Recognition page and upload an image of a plant with suspected diseases.
- Analysis: Our system will process the image using advanced algorithms to identify potential diseases.
- Results: View the results and recommendations for further action.
- Accuracy: Our system utilizes state-of-the-art machine learning techniques for accurate disease detection.
- User-Friendly: Simple and intuitive interface for seamless user experience.
- Fast and Efficient: Receive results in seconds, allowing for quick decision-making.
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Clone the Repository:
git clone https://github.com/cizodevahm/plant-disease-recognition.git cd plant-disease-recognition -
Download the Dataset: Download the dataset from Kaggle and place it in the appropriate directory.
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Run the Application:
streamlit run main.py
The home page provides an overview of the system and its capabilities.
The about page gives detailed information about the dataset used in training the model and other project details.
Upload an image of a plant leaf and let our model predict the disease.
The model is trained on a custom-built Keras model using a dataset of 87K images, categorized into 38 different classes. The dataset is divided into 80/20 ratio for training and validation, with a separate test set for predictions.
- Train Set: 70,295 images
- Validation Set: 17,572 images
- Test Set: 33 images
The dataset can be found here.
This project is licensed under the GPL-3.0 license - see the LICENSE file for details.
- Thanks to the contributors of the original dataset.
- Special thanks to the developers of TensorFlow and Streamlit for providing such powerful tools for machine learning and web applications.