---
pipeline_tag: image-classification
---
---
license: mit
tags:
- image-classification
- pytorch
- resnet50
- agriculture
- plant-disease
- transfer-learning
library_name: pytorch
---

# PlantDoc AI — Plant Disease Classifier (ResNet50)

A 29-class plant disease classifier covering 9 plant species, trained via transfer learning on a frozen ResNet50 (ImageNet) backbone with a custom classification head. Built as the core model behind [PlantDoc AI](https://github.com/yourusername/PlantDoc-AI) — a mobile-first app that lets a farmer photograph a leaf and get a diagnosis and offline voice advice in English, Urdu, or Sindhi.

This model handles classification only. Disease explanations (cause/impact/precautions) are generated separately by an LLM that receives the predicted class name — the LLM never sees the image.

## Model Details

- **Architecture**: ResNet50, ImageNet-pretrained backbone (frozen), custom fully-connected head for 29-class output
- **Framework**: PyTorch
- **Input**: RGB leaf image, resized to 256×256, normalized with standard ImageNet mean/std
- **Output**: Class index (0–28) → disease label
- **Training data**: ~54,000 labeled leaf images across 9 plant species
- **Training**: 5 epochs, Google Colab
- **Validation accuracy**: 97.88%

## Plant Species Covered

Apple, Banana, Tomato, Potato, [add remaining 5 species here]

## Class Labels

> ⚠️ Note: several classes are internally labeled just `"Healthy"` (one per plant, without a plant prefix). The index-to-label mapping below is required to correctly interpret model output — copy it exactly from your `app.js`/`main.py` sequence list.

```python
CLASS_NAMES = [
    "0: ...",
    "1: ...",
    # paste your full 29-class sequence list here, in exact index order
]
```

## How to Use

```python
import torch
from torchvision import models, transforms
from PIL import Image

# Rebuild the architecture (frozen ResNet50 backbone + custom head)
model = models.resnet50(weights=None)
model.fc = torch.nn.Linear(model.fc.in_features, 29)  # 29-class head

# Load trained weights
state_dict = torch.load("checkpoint.pth", map_location="cpu")
model.load_state_dict(state_dict)
model.eval()

# Preprocess an image
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                          std=[0.229, 0.224, 0.225]),
])

img = Image.open("leaf.jpg").convert("RGB")
input_tensor = transform(img).unsqueeze(0)

with torch.no_grad():
    output = model(input_tensor)
    predicted_class = output.argmax(dim=1).item()

print(f"Predicted class index: {predicted_class}")
```

## A Note on Model Behavior

During evaluation, the model frequently confused **Potato Late Blight** with **Tomato Late Blight**. This isn't a failure to distinguish plant species — both diseases are caused by the same pathogen, *Phytophthora infestans*. The confusion reflects genuine shared disease biology rather than a modeling error, and is a useful reminder to investigate misclassifications before treating them as bugs.

## Limitations

- Frozen backbone only — the ResNet50 base was not fine-tuned. Unfreezing and training end-to-end would likely yield a further 1–2% accuracy gain.
- Class naming is not human-readable out of the box (see note above on `"Healthy"` labels); consumers of this model should apply a "Plant - Disease" mapping for display purposes.
- Trained and validated on a fixed dataset; performance on images taken in different lighting, backgrounds, or camera qualities than the training set is untested.

## Intended Use

Educational and assistive tool for early plant disease identification, particularly in low-connectivity settings. Not a substitute for professional agronomic diagnosis in high-stakes commercial farming decisions.

## Links

- Full application (backend + frontend + training notebook): [GitHub — PlantDoc-AI](https://github.com/AbdulSami-Esc/Plantdoc-AI)