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---
language: en
tags:
- pytorch
- computer-vision
- image-classification
- rice-disease
license: mit
---
# VGG16-CNN Rice Disease Classification Model
This model is designed for classifying rice plant diseases using a modified VGG16 architecture with additional CNN layers.
## Model Description
### Architecture
- Base model: VGG16 (pretrained on ImageNet)
- Additional custom CNN layer with:
- Conv2d(512, 64, kernel_size=3)
- ReLU activation
- BatchNorm2d
- MaxPool2d
- Custom classifier with:
- Linear layers (32*3*6 → 1024 → 5)
- Dropout (0.4)
### Task
Image classification for rice plant diseases
### Classes
1. Bacterialblight
2. Blast
3. Brownspot
4. Healthy
5. Tungro
## Training
The model uses transfer learning with a frozen VGG16 backbone.
## Intended Use
- Primary intended use: Rice disease diagnosis through leaf image analysis
- Out-of-scope use: Should not be used for critical agricultural decisions without expert verification
## Input
- RGB images
- Required size: 224x224 pixels
- Preprocessing:
- Normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
## Limitations
Please note that this model should be used as a supportive tool and not as a sole decision-maker for disease diagnosis.
## Model Author
[Your Name/Organization]
## Citation
If you use this model, please cite:
```
@software{vgg_cnn_rice_disease,
title={VGG16-CNN Rice Disease Classification Model},
version={0.1.0},
year={2024}
}
```
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