TomatoGuard 🍅

TomatoGuard is an AI-powered web application designed to detect and classify tomato leaf diseases. It leverages Machine Learning (CNNs) and Computer Vision to help farmers and gardeners identify plant health issues early.

🌟 Features

• Image Classification: Upload an image of a tomato leaf to detect diseases using a pre-trained TensorFlow.js model running entirely in your browser.
• Real-time Detection: Use your webcam for real-time disease detection powered by Roboflow.
• Detailed Information: Get instant results with confidence scores and disease names.
• Responsive Design: Built with React and TailwindCSS for a seamless experience on desktop and mobile.

📊 Dataset

The model was trained on the Tomato Leaf Disease Dataset available on Kaggle.

• Dataset Link: Tomato Leaf Dataset on Kaggle

Classes Detected (10 Classes):

1. Tomato Bacterial Spot
2. Tomato Early Blight
3. Tomato Late Blight
4. Tomato Leaf Mold
5. Tomato Septoria Leaf Spot
6. Tomato Spider Mites (Two-spotted spider mite)
7. Tomato Target Spot
8. Tomato Yellow Leaf Curl Virus
9. Tomato Mosaic Virus
10. Tomato Healthy

🛠️ Tech Stack

• Frontend: React.js, Bootstrap, DaisyUI, TailwindCSS
• Machine Learning:
  • Training: Python, TensorFlow/Keras (Jupyter Notebooks)
  • Inference: TensorFlow.js (Client-side), Roboflow.js
• Tools: npm, React Scripts

🚀 Getting Started

Follow these steps to set up and run the project locally.

Prerequisites

• Node.js (v14 or higher)
• npm (Node Package Manager)

Installation

1. Clone the Repository

   git clone https://github.com/Susreel7/TomatoGuard.git
   cd TomatoGuard/TomatoGuard

2. Install Dependencies

   npm install

   This will install all necessary packages listed in package.json.

Execution

1. Start the Development Server

   npm start

2. Open in Browser The application should automatically open at http://localhost:3000. If not, navigate to that URL in your browser.

🧠 Model Training (Optional)

If you wish to retrain the model or explore the training process:

1. Navigate to the model/ directory.
2. Open image_classification/image_classification.ipynb in Jupyter Notebook or Google Colab.
3. The notebook contains steps to:
   • Download the dataset from Kaggle.
   • Preprocess and augment images.
   • Train a CNN model (MobileNetV2/VGG16).
   • Export the model to TensorFlow.js format (model.json).

Note: The pre-trained model is already located in public/model/, so you do not need to train it to run the app.

📂 Project Structure

TomatoGuard/
├── model/                  # Python notebooks for model training
│   ├── image_classification/
│   └── object_detection/
├── public/
│   ├── model/              # TFJS model files (model.json + shards)
│   └── index.html
├── src/
│   ├── components/         # React components (ImageCard, DescCard)
│   ├── data/               # Static data (disease info)
│   ├── pages/              # Main pages (Classify, Homepage)
│   ├── App.jsx             # Main App component
│   └── index.js            # Entry point
├── documentation/          # Architecture diagrams and docs
├── package.json            # Project dependencies
└── README.md               # Project documentation

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License.