plantdoc-predictor 0.1.1

A Python library for predicting plant diseases from leaf images using trained deep learning models.

# 🌿 PlantDoc Predictor

🌿 PlantDoc-Predictor

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A Python library for predicting plant diseases from leaf images using pre-trained or custom deep learning models.

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🚀 Features

• ✅ Pretrained models included — Ready-to-use architectures like InceptionV3, ResNet50, and MobileNetV2, trained on the 38-class PlantVillage dataset.
• 🧠 Unified API — One interface for both built-in and custom .h5 models.
• 🧩 Custom model support — Load your own model and label mapping JSON.
• 🌱 Extensible — Easily add new crops, datasets, or models via model_registry.json.
• 🧰 Visualization support — Displays prediction confidence and leaf images.
• ⚙️ Cross-platform — Works seamlessly on Windows, macOS, and Linux.

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🧠 Why Use PlantDoc-Predictor

PlantDoc-Predictor was created to reduce repetitive work in plant-disease research.
Researchers and agritech developers often train from scratch — this tool eliminates that friction by providing:

• Pretrained baselines for benchmarking new models.
• Standardized label sets and metadata for reproducibility.
• Plug-and-play inference for agricultural image datasets.
• A unified interface for rapid experimentation and deployment.

Whether you’re a researcher, startup, or precision-agriculture developer, PlantDoc-Predictor simplifies your workflow and lets you focus on innovation — not setup.

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📦 Installation

Install directly via pip:

pip install plantdoc-predictor

## 🧩 Quick Start

```python
from plantdoc_predictor import Predictor, list_available_models

# List built-in models
list_available_models()

# Load predictor using InceptionV3
predictor = Predictor(model_name="inceptionv3", verbose=True)

# Predict a sample image
result = predictor.predict("path/to/leaf_image.jpg")
print(result)

🔄 Loading InceptionV3 model...
✅ Model loaded successfully!

================= Prediction Result =================
📂 Image Path     : path/to/leaf_image.jpg
✅ Predicted Class: Apple___Apple_scab
🔢 Confidence     : 98.42%
🏆 Top-3 Predictions:
   • Apple___Apple_scab                        → 98.42%
   • Apple___Black_rot                         → 0.97%
   • Apple___Cedar_apple_rust                  → 0.43%
=====================================================

{'model': 'inceptionv3', 'label': 'Apple___Apple_scab', 'confidence': 0.9842}

Using a Custom Model

You can use your own trained TensorFlow/Keras model instead of the built-in ones.
predictor = Predictor(
    model_path="models/my_custom_model.h5",
    label_path="models/my_labels.json",
    verbose=True
)

result = predictor.predict("path/to/custom_leaf.jpg", show_plot=True)
print(result)

✅ The library automatically detects input shape and normalizes image data. ✅ You can store your label mapping in a JSON file with the format:

{
  "labels": [
    "Apple___Apple_scab",
    "Apple___Black_rot",
    "Apple___Cedar_apple_rust",
    "Apple___healthy"
  ]
}

🧬 Supported Models

Model Name	Dataset	Input Size	Accuracy	Description
InceptionV3	PlantVillage	299×299	92.8%	High-accuracy model fine-tuned for general plant disease classification.
ResNet50	PlantVillage	224×224	90.3%	Residual CNN architecture with deep feature extraction.
MobileNetV2	PlantVillage	224×224	89.4%	Lightweight model optimized for mobile and edge deployment.
EfficientNetB0	PlantVillage	224×224	91.1%	Modern CNN architecture balancing accuracy and efficiency.

🧩 You can easily extend the library by adding new entries to model_registry.json:

{
  "models": [
    {
      "name": "my_custom_model",
      "path": "models/my_custom_model.h5",
      "labels": "models/my_labels.json",
      "input_size": [224, 224],
      "accuracy": 0.93,
      "description": "Custom fine-tuned model for tomato leaf disease detection"
    }
  ]
}

📝 License

This project is licensed under the MIT License.

You are free to:

• ✅ Use the library for both commercial and academic purposes
• 🔧 Modify, distribute, or integrate it into your own software
• 🌍 Reference and extend it in research or production projects

Just ensure you include the original copyright notice and this license file.

See the full text in the LICENSE file.

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🌍 Contributing

Contributions are warmly welcomed! 🌱

We value community participation to make PlantDoc-Predictor more robust, accurate, and user-friendly.

You can contribute by:

• 🧠 Adding new pretrained or fine-tuned models
• ⚙️ Improving preprocessing or postprocessing modules
• 🧩 Extending model registry or dataset support
• 🧪 Writing new test cases for reproducibility
• 🐛 Fixing bugs and optimizing performance
• 📚 Improving documentation and adding usage examples

🔧 Steps to Contribute

1. Fork this repository
2. Create your feature branch

   git checkout -b feature/your-feature
   

📫 Contact

Author: Subham Divakar
Email: [e-mail]
GitHub: shubham10divakar
PyPI: https://pypi.org/project/plantdoc-predictor/
LinkedIn: linkedin.com/in/subhamdivakar
Project Website: My Site

If you have any questions, collaboration ideas, or model suggestions — feel free to reach out!
You can also open an issue or submit a pull request in the GitHub repository.

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❤️ Acknowledgements

A heartfelt thank you to all the open-source contributors and researchers who made this project possible:

• 🌿 PlantVillage Dataset — for providing an invaluable resource for agricultural disease detection.
• 🤖 TensorFlow/Keras — for powering deep learning model training and inference.
• 🧩 Python Open Source Community — for libraries that make AI tools easier to build.
• 🧪 Researchers & Reviewers — for advancing the field of AI in agriculture.
• 💻 Contributors — for helping test, improve, and document PlantDoc-Predictor.

Your support continues to make plant disease prediction accessible and impactful across the world.

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“Empowering agriculture with AI — one leaf at a time.” 🌾
— Subham Divakar, Creator of PlantDoc-Predictor