PalmOilRipenessDetection-Python/palm_oil_mobile

🌴 Palm Oil Ripeness AI (YOLO26 Mobile)

A professional, high-performance Flutter application powered by the YOLO26 (January 2026) architecture. Designed for palm oil plantation managers, this app utilizes NMS-Free End-to-End detection for maximum efficiency in the field.

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📱 Executive Summary

The Palm Oil Ripeness AI mobile app is a field-ready tool that automates Fresh Fruit Bunch (FFB) assessment. By leveraging YOLO26-Nano, the app achieves a 43% speed increase on mobile CPUs compared to previous generations, eliminating latency bottlenecks and providing instant, high-accuracy grading without internet connectivity.

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🛠 Features

1. Live Inference (NMS-Free Point-and-Scan)

• Real-time Detection: Powered by YOLO26's native one-to-one label assignment, removing the need for post-inference NMS processing.
• Momentum Lock: A robust hysteresis system that "locks" onto fruit bunches to prevent camera stutter and ensure reliable capture.
• Ultra-Low Latency: Optimized for 38.9ms inference on standard mobile hardware.

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" to optimize Oil Extraction Rates (OER).

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🚀 Setup & Installation

Prerequisites

• Flutter SDK (latest stable).
• Ultralytics 8.4.24+ (for model conversion to TFLite/CoreML).
• 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 take advantage of the YOLO26 performance gains, run in Release Mode:

flutter run --release

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🏗 Project Architecture

📁 lib/services/ (Logic Layer)

• tflite_service.dart: An Isolate-based service handling YOLO26 NMS-Free inference. By removing the NMS step, the service reduces UI thread jank by up to 50% compared to legacy v8 models.

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📦 Assets

• assets/best.tflite: The YOLO26-Nano model (Natively NMS-Free).
• 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.