Detectomato is a mobile application built with Flutter and Dart to automatically detect diseases on tomato plant leaves. This app leverages a Convolutional Neural Network (CNN) with the ResNet50 architecture, optimized with TensorFlow Lite to run directly on Android devices for offline use.
- 📸 Capture images or upload photos of tomato leaves
- 🧠 Automatic prediction of 11 common leaf diseases
- 📊 View a history of detection results
- 👤 User authentication (sign up, sign in, sign out)
- 📝 Support and feedback forms
- 💾 Backend powered by Supabase
| Technology | Description |
|---|---|
| Flutter | Mobile app development framework |
| Dart | Primary programming language |
| TensorFlow Lite | On-device image classification model |
| Supabase | Open-source backend service (PostgreSQL, APIs) |
| ResNet50 | CNN architecture for leaf disease classification |
| Kaggle Dataset | Source of training data for the AI model |
The CNN model was developed and trained using a dataset from Kaggle:
🔗 Tomato Disease Dataset – Multiple Sources
Recognized Disease Classes:
- Late Blight
- Early Blight
- Septoria Leaf Spot
- Bacterial Spot
- Leaf Mold
- Target Spot
- Tomato Mosaic Virus
- Tomato Yellow Leaf Curl Virus
- Spider Mites
- Powdery Mildew
- Healthy
- ✅ Done: Offline AI-based disease detection (ResNet50 + TFLite)
- ✅ Done: User login, registration, history, and classification results
- 🟡 In Progress: Adding detailed disease descriptions and treatment recommendations
- 🟡 Future: Integrating geolocation and push notifications
- 👨💻 Sergio Winnero – AI/CNN Model, Flutter, TensorFlow Lite, Frontend, Backend
- 🧪 Samuel Setiawan – Testing, Finalization
- 🎨 Karina Vanya Wardoyo – UI/UX Development