AgriVerse Vision
AgriVerse-Vision is a vision model fine-tuned from google/vit-base-patch16-224 to classify crop health and diagnose 60 plant diseases across 16 crop species.
The model is the primary visual diagnostic component of the AgriVerse agricultural decision-support system.
Performance Highlights
- Test Accuracy: 98.91%
- Test F1-Score (Weighted): 98.90%
- Macro Average Precision / Recall / F1: 0.99 / 0.99 / 0.99
- Test Loss: 0.0483
- Evaluated on: 13,700 held-out test images
Model Overview
- Model Architecture: Vision Transformer (ViT-Base)
- Checkpoint Base:
google/vit-base-patch16-224 - Number of Parameters: ~86M
- Number of Classes: 60
- Input Resolution: 224 x 224 pixels (RGB)
Usage
- Foliar plant disease diagnosis and crop health classification
- Field scouting support and agricultural research
- Visual feature extractor for multimodal agricultural systems
Quickstart
Using transformers Pipeline
from PIL import Image
from transformers import pipeline
classifier = pipeline(
task="image-classification",
model="Armaan457/AgriVerse-Vision"
)
image = Image.open("leaf_image.jpg").convert("RGB")
predictions = classifier(image, top_k=5)
for pred in predictions:
print(f"{pred['label']:<32} Confidence: {pred['score']:.4f}")