PlantAiCure: Upload plant photos to quickly detect diseases using a CNN model, supporting farmers with reliable diagnoses.
PreProcessed DataSet Link :https://drive.google.com/drive/folders/1qYvAW4KHHZp8WUtvjT4gfmbI0eOrxbiu?usp=sharing
🌱 Image-Based Disease Detection: Upload photos of plants to identify diseases. 🧠 Deep Learning with CNN: Uses a CNN model trained on a large dataset to provide accurate diagnoses. 🖼️ User-Friendly Interface: Simple UI for farmers to upload images and receive results instantly. 🔍 Fast and Accurate: Delivers results quickly, helping farmers take action to protect their crops.
Python 🐍 Flask 🌐 TensorFlow/Keras for CNN Model 🧠 HTML/CSS for Frontend 🎨
Follow these steps to set up and run the PlantAiCure application on your local machine.
Python 3.x installed on your system. Installation Clone the Repository git clone [Replace with the actual URL of your repository.]
cd PlantAiCure Create a Virtual Environment
python -m venv venv Activate the Virtual Environment
On Windows:
venv\Scripts\activate On macOS/Linux:
source venv/bin/activate Install the Required Packages
pip install -r requirements.txt Make sure the requirements.txt file includes all the dependencies required to run the application (Flask, TensorFlow, etc.).
Running the Application Ensure you are in the Project Directory:
cd PlantAiCure Start the Flask Application:
python app.py Access the Application:
Open your web browser and navigate to:
arduino Copy code http://127.0.0.1:5000
Feel free to contribute to this project by creating pull requests, reporting issues, or suggesting new features.