---
license: apache-2.0
tags:
  - image-classification
  - keras
  - tomato-leaf-disease
  - plant-disease-detection
  - agriculture
  - deep-learning
datasets:
  - custom-dataset
language:
  - en
---

# 🍅 Tomato Leaf Disease Detection Model (v1)

This model is designed to detect and classify **tomato leaf diseases** using deep learning (TensorFlow/Keras). It is trained on a curated dataset of tomato leaves, including healthy samples and various common diseases.

---

## 🧠 Model Details

- **Framework**: TensorFlow / Keras  
- **Format**: `.h5` (HDF5)  
- **Model Type**: CNN-based image classifier  
- **Input**: RGB image (resized to `256x256` or the model's expected input shape)  
- **Output**: Predicted class label (e.g., Healthy, Early Blight, Late Blight, etc.)

---

## 🏷️ Classes / Labels

Labels used during training are mapped using the `class_indices.json` file.

Example:
```json
{
  "0": "Tomato_Bacterial_spot",
  "1": "Tomato_Early_blight",
  "2": "Tomato_Late_blight",
  "3": "Tomato_Leaf_Mold",
  "4": "Tomato_Septoria_leaf_spot",
  "5": "Tomato_Spider_mites_Two_spotted_spider_mite",
  "6": "Tomato__Target_Spot",
  "7": "Tomato__Tomato_YellowLeaf__Curl_Virus",
  "8": "Tomato__Tomato_mosaic_virus",
  "9": "Tomato_healthy"
}


```

---

## 📁 Files in This Repository

| File                         | Description                             |
| ---------------------------- | --------------------------------------- |
| `best_model.h5`              | Final trained model                     |
| `tomato_disease_model_v1.h5` | Backup or alternate trained model       |
| `class_indices.json`         | Mapping of class names to label indices |

---

## 🚀 How to Use

### Load the Model and Predict

```python
from tensorflow.keras.models import load_model
import json
import numpy as np
from tensorflow.keras.preprocessing import image

# Load model
model = load_model("best_model.h5")

# Load class indices
with open("class_indices.json", "r") as f:
    class_indices = json.load(f)

# Reverse class mapping
class_labels = {v: k for k, v in class_indices.items()}

# Load and preprocess image
img = image.load_img("path_to_image.jpg", target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0

# Make prediction
prediction = model.predict(img_array)
predicted_class = class_labels[np.argmax(prediction)]

print(f"Predicted Disease: {predicted_class}")
```

---

## 📦 Intended Use

This model is intended for:

* Research and prototyping
* Agricultural assistance applications
* Academic projects related to plant disease detection

**Note**: This model is not intended for medical or commercial agricultural decision-making without expert supervision.

---

## 📊 Training Details

* Dataset: Custom dataset including images of tomato leaves with various diseases
* Model: Custom CNN
* Optimizer: Adam
* Loss Function: Categorical Crossentropy
* Accuracy Achieved: Approximately **94%**

---

## 📜 License

This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

---

## 🙋‍♂️ Author

**Abdullah Zunorain**
Email: \[[abdullahzunorain2@gmail.com](mailto:abdullahzunorain2@gmail.com)]
GitHub: [https://github.com/abdullahzunorain](https://github.com/abdullahzunorain)

---

## 📌 Citation

```bibtex
@misc{abdullahzunorain2025tomato,
  title={Tomato Leaf Disease Detection Model (v1)},
  author={Abdullah Zunorain},
  year={2025},
  url={https://huggingface.co/abdullahzunorain/tomato_leaf_disease_det_model_v1},
  note={Hugging Face}
}
```

---

## 🔗 DOI

**DOI**: [10.57967/hf/5733](https://doi.org/10.57967/hf/5733)

```