plantdoc-predictor 1.0.0

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.

🔬 Advanced Features (NEW 🚀) Will be comming in release 0.2.2(TBD)

• 🧠 Full Model Access — Retrieve the complete loaded model for fine-tuning and experimentation.
• ⚙️ Weights Extraction — Access model weights for analysis, comparison, and research.
• 🧩 Layer Introspection — List all layers and inspect architecture programmatically.

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🔥 Feature Extraction (Research-Grade 🚀)

• 📊 Extract intermediate representations from any layer
• 🧪 Enables feature-space SMOTE, clustering, and embedding analysis
• 🧠 Works across all models in the model zoo
• 🔍 Supports custom layer selection

👉 This transforms PlantDoc-Predictor into a feature extraction + research framework, not just an inference tool.

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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
• Multimodal models for plant disease detection that I presented via many conferences.

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

🚀 How to Use plantdoc_predictor

plantdoc_predictor provides an easy-to-use interface for plant disease prediction using multiple pretrained deep learning models trained on the PlantVillage 38-class dataset.

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1️⃣ List Available Models

You can view all available pretrained models using:

from plantdoc_predictor import predictor, Predictor

predictor.list_available_models()

Output:-

Available Models:
-----------------
- inceptionv3_v1      | Input: [299, 299] | Acc: 98.20% | InceptionV3 model fine-tuned on PlantVillage 38-class dataset
- resnet50_v1         | Input: [224, 224] | Acc: 97.80% | ResNet50 fine-tuned on PlantVillage 38-class dataset
- efficientnetb50_v1  | Input: [224, 224] | Acc: 97.80% | EfficientNetB50 fine-tuned on PlantVillage 38-class dataset
- mobilenetv2_v1      | Input: [224, 224] | Acc: 96.80% | Lightweight model for edge/mobile deployment
- densenet121_v1      | Input: [224, 224] | Acc: 98.68% | DenseNet121 fine-tuned on PlantVillage 38-class dataset
- densenet169_v1      | Input: [224, 224] | Acc: 99.68% | DenseNet169 fine-tuned on PlantVillage 38-class dataset
- densenet210_v1      | Input: [224, 224] | Acc: 97.00% | DenseNet210 fine-tuned on PlantVillage 38-class dataset
- vgg16_v1            | Input: [224, 224] | Acc: 96.80% | VGG16 fine-tuned on PlantVillage 38-class dataset
- vgg19_v1            | Input: [224, 224] | Acc: 98.98% | VGG19 fine-tuned on PlantVillage 38-class dataset
- alexnet_v1          | Input: [224, 224] | Acc: 92.80% | AlexNet fine-tuned on PlantVillage 38-class dataset

2. Choose a model from the available ones and use it below.

from plantdoc_predictor import predictor, Predictor
#predictor.list_available_models()

predictor = Predictor(model_name="efficientnetb50_v1", verbose=False)
result = predictor.predict("D:/D/my docs/my docs/projects/plant disease detection on streamlit cloud/streamlit plant disease detection/data/plantvillagedataset/train/color/Blueberry___healthy/0a3f8b2f-9bb1-4da9-85a1-fb5a52c059e2___RS_HL 2478.JPG")
print(result)

Output:-
{
 'model': 'efficientnetb50_v1',
 'label': 'Blueberry___healthy',
 'confidence': 0.9999573230743408
}

when verbose=True in Predictor(model_name="efficientnetb50_v1", verbose=True)

from plantdoc_predictor import predictor, Predictor
#predictor.list_available_models()

predictor = Predictor(model_name="efficientnetb50_v1", verbose=True)
result = predictor.predict("D:/D/my docs/my docs/projects/plant disease detection on streamlit cloud/streamlit plant disease detection/data/plantvillagedataset/train/color/Blueberry___healthy/0a3f8b2f-9bb1-4da9-85a1-fb5a52c059e2___RS_HL 2478.JPG")
print(result)

OUTPUT:-

✔ Using cached file: efficientnetb50_v1.h5
✔ Using cached file: efficientnetb50_v1_labels.json
WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step

================= Prediction Result =================
📂 Image Path     : D:/D/my docs/my docs/projects/plant disease detection on streamlit cloud/streamlit plant disease detection/data/plantvillagedataset/train/color/Blueberry___healthy/0a3f8b2f-9bb1-4da9-85a1-fb5a52c059e2___RS_HL 2478.JPG
🧩 Model Used     : efficientnetb50_v1
✅ Predicted Class: Blueberry___healthy
🔢 Confidence     : 100.00%
{'model': 'efficientnetb50_v1', 'label': 'Blueberry___healthy', 'confidence': 0.9999573230743408}

🧠 Access Full Model now

You can retrieve the full Keras model for advanced use cases like fine-tuning or inspection.

from plantdoc_predictor import Predictor

predictor = Predictor(model_name="resnet50_v1")

model = predictor.get_model()

print(type(model))

Output:
%runfile 'D:/D/my docs/my docs/projects/plantdoc-predictor/plantdoc-predictor/plantdoc_predictor/Test_v1.py' --wdir
2026-03-21 12:39:04.100272: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-03-21 12:39:06.296357: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-03-21 12:39:06.914439: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
<class 'keras.src.models.functional.Functional'>

⚙️ Extract Model Weights

Access raw model weights for research and experimentation.

from plantdoc_predictor import Predictor

predictor = Predictor(model_name="resnet50_v1")

model = predictor.get_model()
#print(type(model))
weights = predictor.get_weights()

print("Total weight tensors:", len(weights))
print("First tensor shape:", weights[0].shape)

Output:
%runfile 'D:/D/my docs/my docs/projects/plantdoc-predictor/plantdoc-predictor/plantdoc_predictor/Test_v1.py' --wdir
Reloaded modules: recursive_additive_attention_v1, predictor
WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Total weight tensors: 320
First tensor shape: (7, 7, 3, 64)

🧩 Layer-wise Weight Info

Get structured information about model weights.

from plantdoc_predictor import Predictor
predictor = Predictor(model_name="resnet50_v1")

model = predictor.get_model()
#print(type(model))
weights = predictor.get_weights()

weights_info = predictor.get_weights_info()

for layer, shapes in weights_info.items():
    print(layer, shapes)

Output:
%runfile 'D:/D/my docs/my docs/projects/plantdoc-predictor/plantdoc-predictor/plantdoc_predictor/Test_v1.py' --wdir
Reloaded modules: recursive_additive_attention_v1, predictor
WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
conv1_conv [(7, 7, 3, 64), (64,)]
conv1_bn [(64,), (64,), (64,), (64,)]
conv2_block1_1_conv [(1, 1, 64, 64), (64,)]
conv2_block1_1_bn [(64,), (64,), (64,), (64,)]
conv2_block1_2_conv [(3, 3, 64, 64), (64,)]
conv2_block1_2_bn [(64,), (64,), (64,), (64,)]
conv2_block1_0_conv [(1, 1, 64, 256), (256,)]
conv2_block1_3_conv [(1, 1, 64, 256), (256,)]
conv2_block1_0_bn [(256,), (256,), (256,), (256,)]
conv2_block1_3_bn [(256,), (256,), (256,), (256,)]
conv2_block2_1_conv [(1, 1, 256, 64), (64,)]
conv2_block2_1_bn [(64,), (64,), (64,), (64,)]
conv2_block2_2_conv [(3, 3, 64, 64), (64,)]
conv2_block2_2_bn [(64,), (64,), (64,), (64,)]
conv2_block2_3_conv [(1, 1, 64, 256), (256,)]
conv2_block2_3_bn [(256,), (256,), (256,), (256,)]
conv2_block3_1_conv [(1, 1, 256, 64), (64,)]
conv2_block3_1_bn [(64,), (64,), (64,), (64,)]
conv2_block3_2_conv [(3, 3, 64, 64), (64,)]
conv2_block3_2_bn [(64,), (64,), (64,), (64,)]
conv2_block3_3_conv [(1, 1, 64, 256), (256,)]
conv2_block3_3_bn [(256,), (256,), (256,), (256,)]
conv3_block1_1_conv [(1, 1, 256, 128), (128,)]
conv3_block1_1_bn [(128,), (128,), (128,), (128,)]
conv3_block1_2_conv [(3, 3, 128, 128), (128,)]
conv3_block1_2_bn [(128,), (128,), (128,), (128,)]
conv3_block1_0_conv [(1, 1, 256, 512), (512,)]
conv3_block1_3_conv [(1, 1, 128, 512), (512,)]
conv3_block1_0_bn [(512,), (512,), (512,), (512,)]
conv3_block1_3_bn [(512,), (512,), (512,), (512,)]
conv3_block2_1_conv [(1, 1, 512, 128), (128,)]
conv3_block2_1_bn [(128,), (128,), (128,), (128,)]
conv3_block2_2_conv [(3, 3, 128, 128), (128,)]
conv3_block2_2_bn [(128,), (128,), (128,), (128,)]
conv3_block2_3_conv [(1, 1, 128, 512), (512,)]
conv3_block2_3_bn [(512,), (512,), (512,), (512,)]
conv3_block3_1_conv [(1, 1, 512, 128), (128,)]
conv3_block3_1_bn [(128,), (128,), (128,), (128,)]
conv3_block3_2_conv [(3, 3, 128, 128), (128,)]
conv3_block3_2_bn [(128,), (128,), (128,), (128,)]
conv3_block3_3_conv [(1, 1, 128, 512), (512,)]
conv3_block3_3_bn [(512,), (512,), (512,), (512,)]
conv3_block4_1_conv [(1, 1, 512, 128), (128,)]
conv3_block4_1_bn [(128,), (128,), (128,), (128,)]
conv3_block4_2_conv [(3, 3, 128, 128), (128,)]
conv3_block4_2_bn [(128,), (128,), (128,), (128,)]
conv3_block4_3_conv [(1, 1, 128, 512), (512,)]
conv3_block4_3_bn [(512,), (512,), (512,), (512,)]
conv4_block1_1_conv [(1, 1, 512, 256), (256,)]
conv4_block1_1_bn [(256,), (256,), (256,), (256,)]
conv4_block1_2_conv [(3, 3, 256, 256), (256,)]
conv4_block1_2_bn [(256,), (256,), (256,), (256,)]
conv4_block1_0_conv [(1, 1, 512, 1024), (1024,)]
conv4_block1_3_conv [(1, 1, 256, 1024), (1024,)]
conv4_block1_0_bn [(1024,), (1024,), (1024,), (1024,)]
conv4_block1_3_bn [(1024,), (1024,), (1024,), (1024,)]
conv4_block2_1_conv [(1, 1, 1024, 256), (256,)]
conv4_block2_1_bn [(256,), (256,), (256,), (256,)]
conv4_block2_2_conv [(3, 3, 256, 256), (256,)]
conv4_block2_2_bn [(256,), (256,), (256,), (256,)]
conv4_block2_3_conv [(1, 1, 256, 1024), (1024,)]
conv4_block2_3_bn [(1024,), (1024,), (1024,), (1024,)]
conv4_block3_1_conv [(1, 1, 1024, 256), (256,)]
conv4_block3_1_bn [(256,), (256,), (256,), (256,)]
conv4_block3_2_conv [(3, 3, 256, 256), (256,)]
conv4_block3_2_bn [(256,), (256,), (256,), (256,)]
conv4_block3_3_conv [(1, 1, 256, 1024), (1024,)]
conv4_block3_3_bn [(1024,), (1024,), (1024,), (1024,)]
conv4_block4_1_conv [(1, 1, 1024, 256), (256,)]
conv4_block4_1_bn [(256,), (256,), (256,), (256,)]
conv4_block4_2_conv [(3, 3, 256, 256), (256,)]
conv4_block4_2_bn [(256,), (256,), (256,), (256,)]
conv4_block4_3_conv [(1, 1, 256, 1024), (1024,)]
conv4_block4_3_bn [(1024,), (1024,), (1024,), (1024,)]
conv4_block5_1_conv [(1, 1, 1024, 256), (256,)]
conv4_block5_1_bn [(256,), (256,), (256,), (256,)]
conv4_block5_2_conv [(3, 3, 256, 256), (256,)]
conv4_block5_2_bn [(256,), (256,), (256,), (256,)]
conv4_block5_3_conv [(1, 1, 256, 1024), (1024,)]
conv4_block5_3_bn [(1024,), (1024,), (1024,), (1024,)]
conv4_block6_1_conv [(1, 1, 1024, 256), (256,)]
conv4_block6_1_bn [(256,), (256,), (256,), (256,)]
conv4_block6_2_conv [(3, 3, 256, 256), (256,)]
conv4_block6_2_bn [(256,), (256,), (256,), (256,)]
conv4_block6_3_conv [(1, 1, 256, 1024), (1024,)]
conv4_block6_3_bn [(1024,), (1024,), (1024,), (1024,)]
conv5_block1_1_conv [(1, 1, 1024, 512), (512,)]
conv5_block1_1_bn [(512,), (512,), (512,), (512,)]
conv5_block1_2_conv [(3, 3, 512, 512), (512,)]
conv5_block1_2_bn [(512,), (512,), (512,), (512,)]
conv5_block1_0_conv [(1, 1, 1024, 2048), (2048,)]
conv5_block1_3_conv [(1, 1, 512, 2048), (2048,)]
conv5_block1_0_bn [(2048,), (2048,), (2048,), (2048,)]
conv5_block1_3_bn [(2048,), (2048,), (2048,), (2048,)]
conv5_block2_1_conv [(1, 1, 2048, 512), (512,)]
conv5_block2_1_bn [(512,), (512,), (512,), (512,)]
conv5_block2_2_conv [(3, 3, 512, 512), (512,)]
conv5_block2_2_bn [(512,), (512,), (512,), (512,)]
conv5_block2_3_conv [(1, 1, 512, 2048), (2048,)]
conv5_block2_3_bn [(2048,), (2048,), (2048,), (2048,)]
conv5_block3_1_conv [(1, 1, 2048, 512), (512,)]
conv5_block3_1_bn [(512,), (512,), (512,), (512,)]
conv5_block3_2_conv [(3, 3, 512, 512), (512,)]
conv5_block3_2_bn [(512,), (512,), (512,), (512,)]
conv5_block3_3_conv [(1, 1, 512, 2048), (2048,)]
conv5_block3_3_bn [(2048,), (2048,), (2048,), (2048,)]
dense [(2048, 38), (38,)]

📝 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