| --- |
| 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) |
