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AtharAbbas993/
Plant_Species_Classification

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Plant Species Classification Model

Model Description

This is a deep learning model for automated classification of flower species using computer vision. The model can identify 5 common flower types with high accuracy.

Model type: Image Classification
Architecture: EfficientNetB0 with custom classification head
Input: 224×224 RGB images
Output: 5-class classification probabilities

Intended Uses

Primary Use Cases

🌿 Educational plant identification tools
📱 Mobile flower recognition applications
🔬 Botanical research and biodiversity monitoring
🌸 Gardening and nature enthusiast apps

Limitations

Trained on only 5 specific flower species
Performance may vary with image quality and lighting conditions
Not suitable for rare or unusual flower varieties

Classes

The model classifies images into 5 flower species:

daisy 🌼 - Classic white petals with yellow center
dandelion 🌞 - Bright yellow composite flowers
rose 🌹 - Layered petals in various colors
sunflower 🌻 - Large yellow flowers with dark centers
tulip 🌷 - Cup-shaped flowers in vibrant colors

Training Data

Dataset: Flowers Recognition from Kaggle
Total Images: ~4,300
Split: 80% training, 20% validation
Augmentation: Rotation, flipping, zooming, brightness adjustment

Performance

Validation Accuracy: >90%
Inference Speed: Real-time capable
Model Size: ~30MB

Usage

python

from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np

# Load and use the model for flower classification
model = load_model('flower_classification_model.h5')

Input Requirements:

Image format: JPEG, PNG
Image size: 224×224 pixels
Color mode: RGB

Ethical Considerations

Intended for educational and research purposes
Should not replace expert botanical identification
Respect privacy when deploying in applications

Citation

If you use this model in your work, please cite:

Plant Species Classification Model by Athar Abbas
https://huggingface.co/AtharAbbas993/Plant_Species_Classification