Ai flower classification presentation

1. AI Flower Classification System
Project Proposal Presentation

2. Introduction
• Flowers exist in many shapes, sizes, and
colors.
• Manual identification is difficult.
• AI can classify flowers using images.
Flower Image Placeholder

3. Problem Statement
• Identifying flowers manually requires
expertise.
• Need for quick, automated classification.

4. Objectives
• Build a machine learning model to classify
flowers.
• Collect and preprocess flower image dataset.
• Train and evaluate the AI model.
• Develop a user-friendly interface.
Flower Image Placeholder

5. Methodology
• Dataset collection from open sources.
• Image preprocessing and augmentation.
• Model training using CNN or transfer learning.
• Testing and evaluation.
• Deployment via web or GUI.

6. Expected Outcomes
• Accurate AI-based flower classifier.
• Simple interface for predictions.
• Hands-on experience in ML and CV.
Flower Image Placeholder

7. Tools & Technologies
• Python, TensorFlow/Keras
• OpenCV, NumPy, Pandas
• Flask/Streamlit for deployment

8. Conclusion
• AI can make flower identification fast and
accurate.
• This project is a practical use of ML & CV.
Flower Image Placeholder