Plant-Identification-using-CNN

Building a Plant Recognition Model using CNN and Flask

1. Data Collection and Preprocessing

   • Gathered a dataset of different plant images
   • Resized images to 128 pixels for consistency

2. Building the CNN Model

   • Constructed a Convolutional Neural Network (CNN) model
   • Set the model's accuracy to around 62%

3. Exporting the Model

   • Exported the trained model in .h5 format

4. Creating a Flask Web Application

   • Installed Flask library
   • Ran the Flask web application
   • Obtained an authentication token

5. Running the Flask App

   • Input the path to the .h5 model in the Flask app
   • Demonstrated the Flask app in action (not live due to time constraints)
   • Showed the app interface where input images can be provided

6. Testing the Model

   • Provided an input image to the Flask app
   • Model successfully detected the plant in the image

7. Conclusion

   • Demonstrated the process of building a plant recognition model using CNN and Flask
   • Showed the successful detection of a plant in an input image

Link to Loom

https://www.loom.com/share/0188f9bfa70043489001fea9e5cfb4f2