AgriFormer Model Prediction
Upload your point cloud data to get phenotypic trait predictions
Upload Point Cloud Data
Drag and drop your .txt file here, or click to browse
Supported formats: .txt (x y z intensity format)
Using demo predictions - Replace with backend API for real inference
About AgriFormer Model
Model Architecture
AgriFormer uses a transformer-based architecture specifically designed for 3D point cloud analysis. It leverages self-attention mechanisms to capture spatial relationships and extract meaningful phenotypic features.
Training Dataset
Trained on over 10,000 annotated point clouds from various crop types including corn, wheat, and soybeans. The model achieves 99.2% accuracy on validation datasets with real-world agricultural data.
Key Features
- Real-time inference (~0.5s per sample)
- Multi-trait prediction capability
- Robust to noise and occlusions
- Transfer learning support
Applications
- Precision agriculture
- Breeding programs
- Growth monitoring
- Yield prediction