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--- |
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license: apache-2.0 |
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datasets: |
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- sharmin3/Rice-Leaf-Disease |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Rice |
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- Classification |
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- SigLIP2 |
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- Type-Count:05 |
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--- |
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# **Rice-Leaf-Disease** 🌾 |
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> **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. |
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> |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Bacterialblight 0.8853 0.9596 0.9210 1585 |
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Blast 0.9271 0.8472 0.8853 1440 |
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Brownspot 0.9746 0.9369 0.9554 1600 |
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Healthy 1.0000 1.0000 1.0000 1488 |
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Tungro 0.9589 0.9977 0.9779 1308 |
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accuracy 0.9477 7421 |
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macro avg 0.9492 0.9483 0.9479 7421 |
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weighted avg 0.9486 0.9477 0.9474 7421 |
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``` |
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### **Disease Categories:** |
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- **Class 0:** Bacterial Blight |
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- **Class 1:** Blast |
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- **Class 2:** Brown Spot |
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- **Class 3:** Healthy |
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- **Class 4:** Tungro |
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--- |
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# **Run with Transformers 🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from transformers.image_utils import load_image |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Rice-Leaf-Disease" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def classify_leaf_disease(image): |
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"""Predicts the disease type in a rice leaf image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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labels = { |
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"0": "Bacterial Blight", |
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"1": "Blast", |
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"2": "Brown Spot", |
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"3": "Healthy", |
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"4": "Tungro" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=classify_leaf_disease, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Rice Leaf Disease Classification 🌾", |
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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." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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# **Intended Use:** |
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The **Rice-Leaf-Disease** model helps in detecting and classifying rice leaf diseases early, supporting: |
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✅ **Farmers & Agriculturists:** Quick disease detection for better crop management. |
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✅ **Agricultural Research:** Monitoring and analyzing plant disease patterns. |
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✅ **AI & Machine Learning Projects:** Applying AI to real-world agricultural challenges. |
