AgriSentry Rice Disease Classifier
A CNN-based disease classification model for rice crops, part of the AgriSentry diagnostic system.
Model Details
Model Description
This is a TensorFlow CNN model trained to classify diseases in rice plants. It identifies three common rice diseases and healthy crop status.
- Model type: Convolutional Neural Network (CNN)
- Framework: TensorFlow/Keras
- Task: Image Classification
- Input: RGB images (224x224 pixels)
- Output: Disease classification with confidence scores
Supported Classes
- Healthy: No disease detected
- Bacterial Leaf Blight: Bacterial infection
- Brown Spot: Fungal leaf disease
- Leaf Blast: Rice blast fungus infection
Model Features
- Input resolution: 224x224 RGB images
- Optimized for rice paddies and field conditions
- Fast inference suitable for mobile/edge deployment
- Confidence scores for each prediction
Usage
Via Hugging Face Inference API
from huggingface_hub import InferenceClient
client = InferenceClient(model="your-username/agrisentry-rice-disease-classifier")
# Load image and send for inference
with open("rice_leaf.jpg", "rb") as f:
result = client.image_classification(f)
print(result)
Via Python (TensorFlow)
import tensorflow as tf
from PIL import Image
import numpy as np
model = tf.keras.models.load_model('rice_cnn.h5')
img = Image.open('rice_leaf.jpg').resize((224, 224))
img_array = np.array(img) / 255.0
prediction = model.predict(np.expand_dims(img_array, axis=0))
Training Data
- Collected from rice-growing regions
- Augmented with rotation, zoom, and brightness variations
- Balanced across disease classes
Performance
- Accuracy: ~94% on validation set
- Optimized for real-world field images
Limitations
- Best performance with well-lit, close-up leaf images
- May require model fine-tuning for new disease variants
- Designed specifically for rice crops
License
MIT License - See LICENSE file for details
Citation
If you use this model, please cite:
@model{agrisentry_rice_2026,
title={AgriSentry Rice Disease Classifier},
author={AgriSentry Team},
year={2026}
}
Disclaimer
This model is a diagnostic tool and should not replace professional agricultural consultation. Always verify results with domain experts.
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