plantdoc-predictor 1.0.1

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 from keras and pytorch.
• 🧩 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 🚀) Added in release 1.0.0 and above.

• 🧠 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
Recursive Additive Attention v1	224×224	99.7%	recursive_additive_attention_cnn	Custom CNN with Recursive Additive Attention (your research model)
ConvNeXt Base v1	224×224	99.1%	convnext	Modern ConvNet architecture inspired by transformers
ConvNeXt Small v1	224×224	99.5%	convnext	Lightweight ConvNeXt variant with high accuracy
ConvNeXt Tiny v1	224×224	99.23%	convnext	Efficient ConvNeXt model for faster inference
Swin Base Patch4 Window7	224×224	99.1%	swin	Hierarchical Vision Transformer with shifted windows
Swin Tiny Patch4 Window7	224×224	99.1%	swin	Lightweight Swin Transformer for efficient inference
ViT Base 16 v1	224×224	99.1%	vit	Vision Transformer base model
ViT Large 16 v1	224×224	99.1%	vit	Larger ViT model with higher capacity
ViT Small 16 v1	224×224	99.1%	vit	Smaller ViT model for faster inference
ViT Tiny 16 v1	224×224	99.1%	vit	Lightweight Vision Transformer
DenseNet169 v1	224×224	99.68%	densenet169_v1	Best performing DenseNet variant
VGG19 v1	224×224	98.98%	vgg19_v1	Deep VGG architecture with strong performance
DenseNet121 v1	224×224	98.68%	densenet121_v1	Dense connectivity for efficient feature reuse
InceptionV3 v1	299×299	98.2%	inception	Inception architecture with multi-scale feature extraction
ResNet50 v1	224×224	97.8%	resnet50	Residual network for deep feature learning
EfficientNetB50 v1	224×224	97.8%	efficientnet	Efficient scaling of CNN architecture
DenseNet210 v1	224×224	97.0%	densenet210_v1	Very deep DenseNet variant
MobileNetV2 v1	224×224	96.8%	mobilenetv2	Mobile-friendly lightweight architecture
VGG16 v1	224×224	96.8%	vgg16_v1	Classic deep CNN architecture
AlexNet v1	224×224	92.8%	alexnet_v1	Early CNN baseline model

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🧪 Research Models (Published Work)

PlantDoc-Predictor also includes models derived from peer-reviewed research papers, enabling reproducibility and direct comparison with published work.

These models represent novel architectures and contributions to the field of plant disease classification.

Model	Paper	Authors	Accuracy	Description
Recursive Additive Attention v1	IEEE Paper	Subham Divakar, Rojalina Priyadarshini	99.70%	Custom CNN with Recursive Additive Attention mechanism for enhanced feature interaction and classification performance

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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 Added in release 1.0.0 onwards
• ConvNeXt architectures — e.g., convnext_tiny, convnext_small, convnext_base Added in release 1.0.0 onwards
• Hybrid CNN–Transformer models Added in release 1.0.0 onwards
• Swin Transformers — e.g., swin_tiny_patch4_window7_224 Added in release 1.0.0 onwards
• 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
Recursive Additive Attention v1	~12–15M*	224×224	99.7%	Research-grade model with attention (best overall)
ConvNeXt Base v1	~89M	224×224	99.1%	Modern CNN alternative to transformers
ConvNeXt Small v1	~50M	224×224	99.5%	High accuracy with better efficiency
ConvNeXt Tiny v1	~28M	224×224	99.23%	Efficient modern CNN
Swin Base Patch4 Window7	~88M	224×224	99.1%	Hierarchical Vision Transformer
Swin Tiny Patch4 Window7	~28M	224×224	99.1%	Lightweight transformer
ViT Large 16 v1	~307M	224×224	99.1%	Maximum capacity transformer
ViT Base 16 v1	~86M	224×224	99.1%	Standard transformer baseline
ViT Small 16 v1	~48M	224×224	99.1%	Balanced transformer
ViT Tiny 16 v1	~22M	224×224	99.1%	Lightweight transformer
DenseNet169 v1	~14M	224×224	99.68%	Best classical CNN
VGG19 v1	~144M	224×224	98.98%	High-capacity CNN
DenseNet121 v1	~8M	224×224	98.68%	Efficient deep CNN
InceptionV3 v1	~23M	299×299	98.2%	Multi-scale feature extraction
ResNet50 v1	~25M	224×224	97.8%	Deep residual learning
EfficientNetB50 v1	~30M	224×224	97.8%	Accuracy-efficiency tradeoff
DenseNet210 v1	~20M	224×224	97.0%	Very deep dense architecture
MobileNetV2 v1	~3.5M	224×224	96.8%	Mobile / edge devices
VGG16 v1	~138M	224×224	96.8%	Benchmark model
AlexNet v1	~60M	224×224	92.8%	Historical baseline

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🏆 Model Performance Leaderboard

Top performing models on the PlantVillage 38-class dataset.

Rank	Model	Accuracy
🥇	Recursive Additive Attention v1	99.70%
🥈	DenseNet169 v1	99.68%
🥉	ConvNeXt Small v1	99.50%
4	ConvNeXt Tiny v1	99.23%
5	ConvNeXt Base v1	99.10%
6	Swin Base Patch4 Window7	99.10%
7	Swin Tiny Patch4 Window7	99.10%
8	ViT Base 16 v1	99.10%
9	ViT Large 16 v1	99.10%
10	ViT Small 16 v1	99.10%
11	ViT Tiny 16 v1	99.10%
12	VGG19 v1	98.98%
13	DenseNet121 v1	98.68%
14	InceptionV3 v1	98.20%
15	ResNet50 v1	97.80%
16	EfficientNetB50 v1	97.80%
17	DenseNet210 v1	97.00%
18	MobileNetV2 v1	96.80%
19	VGG16 v1	96.80%
20	AlexNet v1	92.80%

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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:
-----------------
- rec_add_attention_v1 | Input: [224, 224] | Acc: 99.70% | Recursive Additive Attention CNN (best overall model)
- convnext_base_v1     | Input: [224, 224] | Acc: 99.10% | ConvNeXt Base fine-tuned on PlantVillage dataset
- convnext_small_v1    | Input: [224, 224] | Acc: 99.50% | ConvNeXt Small (high accuracy + efficient)
- convnext_tiny_v1     | Input: [224, 224] | Acc: 99.23% | Lightweight ConvNeXt for fast inference
- swin_base_patch4_window7 | Input: [224, 224] | Acc: 99.10% | Swin Transformer (hierarchical vision transformer)
- swin_tiny_patch4_window7 | Input: [224, 224] | Acc: 99.10% | Lightweight Swin Transformer
- vit_base_16_v1       | Input: [224, 224] | Acc: 99.10% | Vision Transformer (base)
- vit_large_16_v1      | Input: [224, 224] | Acc: 99.10% | Vision Transformer (large)
- vit_small_16_v1      | Input: [224, 224] | Acc: 99.10% | Vision Transformer (small)
- vit_tiny_16_v1       | Input: [224, 224] | Acc: 99.10% | Vision Transformer (tiny)
- densenet169_v1       | Input: [224, 224] | Acc: 99.68% | Best-performing classical CNN
- vgg19_v1             | Input: [224, 224] | Acc: 98.98% | Deep VGG architecture
- densenet121_v1       | Input: [224, 224] | Acc: 98.68% | Efficient DenseNet variant
- inceptionv3_v1       | Input: [299, 299] | Acc: 98.20% | InceptionV3 with multi-scale feature extraction
- resnet50_v1          | Input: [224, 224] | Acc: 97.80% | Residual learning-based CNN
- efficientnetb50_v1   | Input: [224, 224] | Acc: 97.80% | EfficientNet balancing accuracy & efficiency
- densenet210_v1       | Input: [224, 224] | Acc: 97.00% | Deep DenseNet architecture
- mobilenetv2_v1       | Input: [224, 224] | Acc: 96.80% | Mobile/edge optimized model
- vgg16_v1             | Input: [224, 224] | Acc: 96.80% | Classic CNN baseline
- alexnet_v1           | Input: [224, 224] | Acc: 92.80% | Early CNN baseline

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