plantdoc-predictor 0.2.1

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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🧬 Supported Models

PlantDoc-Predictor includes a diverse model zoo of pretrained CNN architectures fine-tuned on the PlantVillage 38-class plant disease dataset in 0.2.1 version onwards.

These models are automatically downloaded from the remote registry when first used, making them ready for plug-and-play inference.

Model Name	Input Size	Accuracy	Preprocessing	Description
InceptionV3 v1	299×299	98.2%	inception	InceptionV3 model fine-tuned on the PlantVillage 38-class dataset
ResNet50 v1	224×224	97.8%	resnet50	Deep residual network enabling powerful feature extraction
EfficientNetB50 v1	224×224	97.8%	efficientnet	EfficientNet architecture balancing accuracy and computational efficiency
MobileNetV2 v1	224×224	96.8%	mobilenetv2	Lightweight model designed for mobile and edge deployment
DenseNet121 v1	224×224	98.68%	densenet121_v1	Dense connectivity architecture improving gradient flow and feature reuse
DenseNet169 v1	224×224	99.68%	densenet169_v1	High-performance DenseNet variant achieving the best accuracy in the model zoo
DenseNet210 v1	224×224	97.0%	densenet210_v1	Very deep DenseNet architecture for advanced feature extraction
VGG16 v1	224×224	96.8%	vgg16_v1	Classic deep CNN architecture useful for benchmarking experiments
VGG19 v1	224×224	98.98%	vgg19_v1	Deeper VGG architecture providing strong classification performance
AlexNet v1	224×224	92.8%	alexnet_v1	Early CNN architecture useful as a historical baseline model

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🚀 Upcoming Models

PlantDoc-Predictor is actively expanding its Model Zoo.
Future releases will include modern vision architectures and transformer-based models to further improve performance and research capabilities.

Some of the upcoming models planned for integration include:

• Vision Transformers (ViT) — e.g., vit_base_patch16_224, vit_large_patch16_224
• ConvNeXt architectures — e.g., convnext_tiny, convnext_small, convnext_base
• Hybrid CNN–Transformer models
• Swin Transformers — e.g., swin_tiny_patch4_window7_224
• EfficientNetV2 family

Planned release very month and many more features to get added as well.

These additions will allow researchers and developers to experiment with state-of-the-art deep learning architectures for plant disease classification.

Stay tuned for future releases as the PlantDoc Model Zoo continues to grow. 🌿

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🌍 Community & Contact

PlantDoc-Predictor is growing into a widely used open-source machine learning library for plant disease classification.
Community participation is essential to help expand the PlantDoc Model Zoo and improve the ecosystem.

📈 Current Usage

The library is actively used by researchers, developers, and agritech enthusiasts worldwide.

These downloads reflect the growing interest in AI-powered plant disease detection and encourage further development of the library.

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🤝 Contribute to the Project

We warmly welcome contributions from the community to help improve and expand PlantDoc-Predictor.

You can contribute by:

• 🧠 Adding new pretrained models to the PlantDoc Model Zoo
• ⚙️ Improving preprocessing or inference pipelines
• 📊 Benchmarking new architectures
• 🧪 Adding test cases and improving reproducibility
• 📚 Improving documentation and tutorials
• 🐛 Fixing bugs and optimizing performance

If you are interested in contributing to the development of this widely used ML library, feel free to open a pull request or start a discussion.

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📫 Contact the Author

If you have ideas, questions, research collaboration proposals, or model suggestions, feel free to reach out.

Author: Subham Divakar
📧 Email: [e-mail]
🐙 GitHub: https://github.com/shubham10divakar

We welcome researchers, ML engineers, and contributors who want to help make PlantDoc-Predictor the largest open-source model zoo for plant disease detection. 🌿

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📊 Model Zoo Comparison

The following table compares the pretrained models included in PlantDoc-Predictor .

Model	Parameters	Input Size	Accuracy	Best Use Case
InceptionV3 v1	~23M	299×299	98.2%	High accuracy classification
ResNet50 v1	~25M	224×224	97.8%	Deep feature extraction
EfficientNetB50 v1	~30M	224×224	97.8%	Balanced accuracy & efficiency
MobileNetV2 v1	~3.5M	224×224	96.8%	Edge / mobile deployment
DenseNet121 v1	~8M	224×224	98.68%	Efficient deep CNN
DenseNet169 v1	~14M	224×224	99.68%	Highest accuracy
DenseNet210 v1	~20M	224×224	97.0%	Deep dense feature learning
VGG16 v1	~138M	224×224	96.8%	Baseline CNN benchmark
VGG19 v1	~144M	224×224	98.98%	Deeper VGG architecture
AlexNet v1	~60M	224×224	92.8%	Historical CNN baseline

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🏆 Model Performance Leaderboard

Top performing models on the PlantVillage 38-class dataset.

Rank	Model	Accuracy
🥇	DenseNet169 v1	99.68%
🥈	VGG19 v1	98.98%
🥉	DenseNet121 v1	98.68%
4	InceptionV3 v1	98.2%
5	ResNet50 v1	97.8%
6	EfficientNetB50 v1	97.8%
7	DenseNet210 v1	97.0%
8	MobileNetV2 v1	96.8%
9	VGG16 v1	96.8%
10	AlexNet v1	92.8%

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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"
  ]
}

## 📝 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](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  
   ```bash
   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