flower-finder

datasets

• https://universe.roboflow.com/my-stuff-0wmth/flowerdetecter-v2/dataset/1
• https://worldfloraonline.org/classification
• https://www.kaggle.com/datasets/abhayayare/flower-dataset/data
• https://www.kaggle.com/datasets/aksha05/flower-image-dataset
• https://www.kaggle.com/datasets/kausthubkannan/5-flower-types-classification-dataset
• https://www.kaggle.com/datasets/nunenuh/pytorch-challange-flower-dataset
• https://www.tensorflow.org/datasets/catalog/i_naturalist2021

tutorials

• https://www.youtube.com/watch?v=h6TJiGrYINk
• https://www.youtube.com/watch?v=Hr06nSA-qww

to-do

-[]

flower id + visualization

project overview

A comprehensive machine learning application that enables users to:

1. Capture or upload a flower image
2. Automatically identify the flower's common and scientific names
3. Generate a 3D model representation of the flower
4. Create a pixelated font/icon of the identified flower

Technical Components

1. Image Recognition Model

• Architecture**: Convolutional Neural Network (CNN)
• Base Model: ResNet50 or EfficientNet
• Transfer Learning from pre-trained botanical datasets
• High-accuracy flower species classification

2. Training Data Requirements:
   • Comprehensive flower image dataset
   • Minimum 100 images per flower species
   • Diverse angles, lighting conditions, and backgrounds
   • Sources:
     • iNaturalist
     • PlantNet
     • Custom augmented datasets
3. Classification Metrics:
   • Accuracy Target: >95%
   • Top-3 Species Confidence
   • Probabilistic species matching
4. Flower Database Integration
   Database Contents:
   • Scientific name
   • Common name(s)
   • Botanical family
   • Native region
   • Bloom season
   • Additional metadata
5. 3D Model Generation Techniques
   • Photogrammetry-based reconstruction
   • Procedural 3D modeling
   • Machine learning-assisted shape generation Output Specifications
   • Formats: .OBJ, .GLTF, .FBX
   • Low-poly and high-poly variants
   • Texture mapping
   • Color accuracy matching original image
6. Pixelated Font/Icon Generation
   • Generation Methods
     • Automated pixel art conversion
     • Color palette extraction from original image
     • Adaptive resolution scaling
     • Preservation of flower's key structural elements
   • Output Specifications
     • Multiple sizes (16x16, 32x32, 64x64 pixels)
     • Transparent background
     • Color fidelity to original flower

Technology Stack

• Backend: Python
• ML Frameworks: TensorFlow, PyTorch
• 3D Modeling: Blender API
• Web Framework: Flask/FastAPI
• Frontend: React/Vue.js
• Mobile: React Native/Flutter

Ethical Considerations

• Respect botanical research intellectual property
• Ensure accurate scientific representation
• Provide educational context with identification

Potential Applications

• Botanical research
• Educational tools
• Gardening applications
• Nature photography assistants
• Ecological biodiversity tracking

Development Roadmap

1. Dataset Compilation (3 months)
2. ML Model Training (4 months)
3. 3D/Pixel Generation Algorithms (3 months)
4. Integration & User Interface (2 months)
5. Testing & Refinement (3 months)

Estimated Resources

• Computational Resources: High-performance GPU cluster
• Storage: 10+ TB for datasets
• Team:
  • 2 Machine Learning Engineers
  • 1 3D Modeling Specialist
  • 1 Frontend Developer
  • 1 Database Architect