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Rice-Leaf-Disease 🌾

Rice-Leaf-Disease is an image classification model fine-tuned from google/siglip2-base-patch16-224 for detecting and categorizing diseases in rice leaves. It is built using the SiglipForImageClassification architecture and helps in early identification of plant diseases for better crop management.

Classification Report:
 precision recall f1-score support

Bacterialblight 0.8853 0.9596 0.9210 1585
 Blast 0.9271 0.8472 0.8853 1440
 Brownspot 0.9746 0.9369 0.9554 1600
 Healthy 1.0000 1.0000 1.0000 1488
 Tungro 0.9589 0.9977 0.9779 1308

 accuracy 0.9477 7421
 macro avg 0.9492 0.9483 0.9479 7421
 weighted avg 0.9486 0.9477 0.9474 7421

👁 download (1).png

Disease Categories:

• Class 0: Bacterial Blight
• Class 1: Blast
• Class 2: Brown Spot
• Class 3: Healthy
• Class 4: Tungro

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Run with Transformers 🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Rice-Leaf-Disease"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def classify_leaf_disease(image):
 """Predicts the disease type in a rice leaf image."""
 image = Image.fromarray(image).convert("RGB")
 inputs = processor(images=image, return_tensors="pt")
 
 with torch.no_grad():
 outputs = model(**inputs)
 logits = outputs.logits
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
 
 labels = {
 "0": "Bacterial Blight",
 "1": "Blast",
 "2": "Brown Spot",
 "3": "Healthy",
 "4": "Tungro"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=classify_leaf_disease,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Rice Leaf Disease Classification 🌾",
 description="Upload an image of a rice leaf to identify if it is healthy or affected by diseases like Bacterial Blight, Blast, Brown Spot, or Tungro."
)

# Launch the app
if __name__ == "__main__":
 iface.launch()

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Intended Use:

The Rice-Leaf-Disease model helps in detecting and classifying rice leaf diseases early, supporting:
✅ Farmers & Agriculturists: Quick disease detection for better crop management.
✅ Agricultural Research: Monitoring and analyzing plant disease patterns.
✅ AI & Machine Learning Projects: Applying AI to real-world agricultural challenges.

Base model

google/siglip2-base-patch16-224

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