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palm_oil_mobile/README.md

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-# palm_oil_mobile
+# Palm Oil Ripeness AI - Mobile App
 
-A new Flutter project.
+This Flutter application ports the AI-driven palm oil ripeness detection system to mobile devices. It allows for gallery-based analysis with local history storage and health alerts.
 
-## Getting Started
+## 🛠 Features
+- **AI Inference**: On-device TFLite processing for ripeness classification.
+- **Gallery Analysis**: Pick photos directly from the device gallery.
+- **History Vault**: Local storage of detection results using SQFlite.
+- **Health Alerts**: Immediate UI feedback for abnormal bunches.
 
-This project is a starting point for a Flutter application.
+## 🚀 Setup & Installation
 
-A few resources to get you started if this is your first Flutter project:
+### Prerequisites
+- [Flutter SDK](https://docs.flutter.dev/get-started/install) installed and added to your PATH.
+- Android Studio or VS Code with Flutter extension.
+- A physical Android device connected via USB with "Developer Options" and "USB Debugging" enabled.
 
-- [Learn Flutter](https://docs.flutter.dev/get-started/learn-flutter)
-- [Write your first Flutter app](https://docs.flutter.dev/get-started/codelab)
-- [Flutter learning resources](https://docs.flutter.dev/reference/learning-resources)
+### 1. Initialize Project
+Navigate to the mobile project directory and fetch dependencies:
+```bash
+cd palm_oil_mobile
+flutter pub get
+```
 
-For help getting started with Flutter development, view the
-[online documentation](https://docs.flutter.dev/), which offers tutorials,
-samples, guidance on mobile development, and a full API reference.
+### 2. Run the App
+To test the app with full AI performance, run it in **Release Mode**:
+```bash
+flutter run --release
+```
+
+## 🧪 Testing Instructions
+
+1.  **Launch the App**: Open the app on your device.
+2.  **Select Gallery Analysis**: Tap on the **Analyze Gallery** card.
+3.  **Pick a Photo**: Grant gallery permissions and select a photo of a palm oil bunch.
+4.  **Observe Results**:
+    *   The app will display "Initializing AI..." while processing.
+    *   The detected grade and confidence score will appear.
+    *   🔴 **Health Alert**: If "Empty_Bunch" or "Abnormal" is detected, a red warning box will show.
+5.  **Check History**: Navigate back to the Home screen and select **History Vault** to see your past detections.
+6.  **Re-examine**: Tap on any history card to view the detailed summary.
+
+## 🏗 Project Structure (Flutter Beginner's Guide)
+
+As you're new to Flutter, here’s a map of where the important logic lives. All your custom code is inside the `lib/` folder:
+
+### 1. `lib/main.dart`
+The **entry point** of the application. This is where the app theme is defined and the first screen (`HomeScreen`) is launched.
+
+### 2. `lib/models/`
+*   **`palm_record.dart`**: Defines the "blueprint" for a detection result. If you want to store more data (like GPS coordinates or weather), you'd add fields here.
+
+### 3. `lib/services/`
+This folder contains "behind-the-scenes" logic:
+*   **`yolo_service.dart`**: Wraps the AI model. This is where the TFLite model is initialized and where image inference happens.
+*   **`database_helper.dart`**: Manages the local SQLite database (`palm_oil_history.db`). Change this if you need to update the database schema.
+
+### 4. `lib/screens/`
+This is where the **UI** lives:
+*   **`home_screen.dart`**: The main dashboard.
+*   **`analysis_screen.dart`**: The UI for image selection, showing the loading spinner, and displaying the AI results.
+*   **`history_screen.dart`**: The list view for previous records and the detail modal.
+
+### 5. `assets/`
+Located at the root of the project. This contains your non-code files:
+*   `models/best.tflite`: The trained brain of the app.
+*   `labels/labels.txt`: The names of the classes the model can recognize.
+
+## ⚙️ Configuration
+*   **`pubspec.yaml`**: The project's manifest. This is where you add new dependencies (like camera or GPS plugins) and register new assets.