palmoil-mobile

palm_oil_mobile

A Flutter app that runs on-device YOLO26 (NMS-free) inference to detect palm oil fresh fruit bunch (FFB) ripeness against MPOB grading standards — fully offline, no server or network calls required. Built for FFB harvesters to scan bunches in the field and compare their own judgment against the model's.

Detection classes

Ripe, Unripe, Underripe, Overripe, Abnormal, Empty_Bunch (MPOB standard).

Features

• Three capture modes:
  • Analyze Gallery — pick one or more photos from the gallery and run inference on each as a batch.
  • Snap & Analyze — manual high-res camera capture.
  • Live Inference — real-time "point-and-scan": an auto-lock state machine detects a steady target, captures a high-res still, and analyzes it automatically.
• Batch reporting — every scan is grouped into a batch (one per capture-screen visit, or per multi-image gallery pick). Batches show a KPI dashboard (frame count, total detections, avg confidence, avg inference time), an MPOB class-distribution bar, and a frame thumbnail grid.
• History Vault — grouped, filterable, sortable history of every batch, with swipe-to-delete and date-range filtering.
• Harvester ground-truth corrections — on any photo's detail screen, a harvester can compare the AI's inference against their own judgment and correct individual detected fruit. Corrections are additive: the AI's original inference is never edited or overwritten, and a photo can be re-corrected any number of times, each one recorded separately — intended to eventually feed a cloud-based retraining pipeline.
• Light/dark theme — a custom teal-green/amber palette with a system-following default and an in-app toggle (Light/Dark/System), persisted across launches.

Getting started

flutter pub get
flutter run                      # run on a connected device/emulator
flutter build apk --debug        # debug build
flutter build apk                # release build
flutter analyze                  # static analysis

The bundled model (assets/best.tflite) and labels (assets/labels.txt) are loaded at runtime — no backend to start, no network required.

Regenerating the app icon

The launcher icon is generated from assets/icon_light.png via flutter_launcher_icons (config lives in pubspec.yaml). After changing the source image, regenerate with:

dart run flutter_launcher_icons

Architecture

See CLAUDE.md for a detailed breakdown of the isolate-based inference pipeline, the live-capture auto-lock state machine, the SQLite schema (batches, corrections), and screen-by-screen notes — written for AI coding agents working in this repo, but equally useful as an architecture reference for humans.

Requirements

• Flutter SDK ^3.11.1
• Android (primary target — this is where the app is actively tested); iOS builds are untested (no current access to iOS hardware)