PalmOilRipenessDetection-Python/palm_oil_mobile

Palm Oil Ripeness AI - Mobile App

A professional, high-performance Flutter application designed for palm oil plantation managers and harvesters. This app provides real-time and static AI-driven ripeness detection, enabling data-driven harvesting decisions directly in the field.

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📱 Executive Summary

The Palm Oil Ripeness AI mobile app is a field-ready tool that automates the assessment of Fresh Fruit Bunches (FFB). By leveraging on-device Deep Learning (TFLite), the app eliminates the need for internet connectivity in remote plantations, providing instant grading and health alerts to optimize yield and minimize loss from premature harvest.

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🛠 Features

1. Live Inference (Point-and-Scan)

• Real-time Detection: Utilizes an atomic idle-lock mechanism to process camera streams at high speed.
• Momentum Lock: A robust hysteresis system that "locks" onto fruit bunches to prevent camera stutter and ensure reliable capture.
• Auto-Capture: Automatically triggers a high-resolution snapshot once a stable detection is confirmed.

2. Snap & Analyze (Manager's Manual Mode)

• High-Res Accuracy: Manual shutter mode using ResolutionPreset.high for maximum detail.
• Direct JPEG Decoding: Skips YUV conversion for the most accurate industrial-grade inference.
• Auto-Reset Workflow: Seamlessly clears previous results after acknowledgment to allow rapid sequential captures.

3. Analyze Gallery

• Allows managers to analyze previously captured photos from the device's storage.
• Includes a scanning animation and detailed bounding box overlays.

4. History Vault & Result Persistence

• SQLite Storage: Every analysis is stored locally with metadata (ripeness class, confidence, coordinates).
• Image Archiving: Automatically copies and persists captured images to the ApplicationDocumentsDirectory.

5. Industrial Alerts & Summaries

• Health Alert: 🔴 Red warnings for "Abnormal" or "Empty_Bunch" detections.
• Yield Warning: 🟠 Orange alerts for "Unripe" or "Underripe" fruit, highlighting "Potential Yield Loss".

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🚀 Setup & Installation

Prerequisites

• Flutter SDK (latest stable).
• Android Studio / VS Code with Flutter & Dart extensions.
• A physical Android/iOS device (Camera required; Emulators NOT recommended).

1. Initialize Project

# Navigate to the mobile project directory
cd palm_oil_mobile

# Fetch dependencies
flutter pub get

2. Run the App

To achieve real-time performance, it is highly recommended to run in Release Mode:

flutter run --release

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🏗 Project Architecture

📁 lib/screens/ (UI Layer)

• home_screen.dart: Central navigation hub with card-based dashboard.
• live_analysis_screen.dart: Advanced camera stream handler with momentum locking logic.
• static_capture_screen.dart: Dedicated manual capture workflow with industrial summaries.
• analysis_screen.dart: Gallery-based interface with scanning overlays.
• history_screen.dart: Secure vault for reviewing past palm records.

📁 lib/services/ (Logic Layer)

• tflite_service.dart: A persistent Isolate-based service that handles all AI inference (static and stream-based) without blocking the UI thread.
• database_helper.dart: Manages the local SQLite database for perpetual history.

📁 lib/models/ (Data Layer)

• palm_record.dart: Schema definition for detection history and bounding box coordinates.

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📦 Assets

• assets/best.tflite: The YOLO-based ripeness detection model.
• assets/labels.txt: Class definitions (Ripe, Unripe, Underripe, Overripe, Abnormal, Empty_Bunch).

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⚠️ Requirements & Permissions

• Camera: Required for Live and Static analysis.
• Storage: Required to save/load photos and database records.