Mobile Fishing App: Technical Solutions
We develop mobile applications for anglers that combine offline waterbody maps, a bite forecast based on weather data and machine learning, and a catch logbook with AR measurement. This sits at the intersection of several technical domains, and we package everything into a single turnkey product. With over 5 years of experience and 30+ published projects, an average App Store rating of 4.8 stars, we are a trusted partner for fishing app development. We guarantee timely delivery and post-launch support for 12 months. Our developers are certified in iOS and Android development. Get a consultation for your project.
How We Implement Offline Waterbody Maps
The app's core is a map with waterbodies, fishing spots, and offline capability. For offline mode we use the MBTiles format (SQLite with embedded tiles). The user downloads the map of a selected region in advance. On iOS we employ Mapbox Maps SDK with OfflineManager.downloadStylePack + TilesetDescriptor. On Android we use Mapbox or OSMDroid with a tile cache. Native Mapbox SDK is 3 times faster than WebView with Leaflet for tile rendering and GPS tracking. For budget projects a WebView with Leaflet and OSM tiles works, but native experience suffers, especially when recording a GPS track.
Fishing spots are user POIs (fishing_spots) with coordinates, name, waterbody type (river/lake/reservoir/sea), and preferred tackle. They are stored in the cloud and synced to SQLite for offline use. Vector layers (spawning zones, restricted areas) are GeoJSON from the server, rendered as MGLFillLayer (iOS) or PolygonOverlay (MapKit).
The choice of offline map technology depends on the trade-off between nativity and cost. Mapbox SDK is great for projects requiring gestures and customization, but after hitting download limits a paid subscription is needed. OSMDroid is free but lacks vector layers without custom work. Leaflet via WebView is cross-platform but suffers performance during GPS tracking — for apps with active tracking, a native solution is better.
Offline Map Module Architecture
The module consists of a sync layer (SyncLayer), tile cache layer (TileCache), and map rendering layer (MapRenderer). SyncLayer downloads MBTiles and GeoJSON, TileCache stores tiles in SQLite, and MapRenderer renders layers using OpenGL via Mapbox or OSMDroid.Bite Forecast Requires ML
Fish activity prediction combines factors: atmospheric pressure (trend), moon phase, water temperature, time of day, wind. We use a weighted sum of factors with a score of 1–5 'fish'. But weights are empirical. To improve accuracy, we train an ML model on catch log data. After introducing ML, forecast accuracy improved by 18% compared to a purely empirical model (the ML model is 1.2 times more accurate). Datasets: OpenStreetMap for geodata and Core ML for the iOS model.
| Platform | Model | Average Accuracy | Model Size |
|---|---|---|---|
| iOS | CoreML (iNaturalist) | 75% for over 120 species | 4–6 MB |
| Android | ML Kit Custom Model | 72% for over 80 species | 6–8 MB |
Catch Logbook with AR and ML
Catch recording includes fish species (selection from a reference with over 120 species + photo), weight/length, tackle (10+ types), bait, depth, coordinates, and automatic weather from an API. Fish photo — with automatic measurement via AR (RealityKit ARWorldTrackingConfiguration + MLModel for size estimation based on a reference object). Species identification by photo — CoreML model (trained on iNaturalist dataset) for iOS, ML Kit Custom Model for Android. Accuracy 70–80% for common species — sufficient for pre-filling the form; the user corrects it. Season statistics: best spots on a heatmap, top species, comparison with previous year. We use Swift Charts (iOS 16+) or MPAndroidChart for graphs.
Fishing Community
Public catch feed with option to hide exact coordinates ('waterbody without point'). Angler ranking by catch weight. Forum for waterbodies tied to geolocation. Privacy: 'secret spot' — coordinate with radius noise ±500 m when published; real coordinate stays in personal logbook.
Development Process
- Analytics and prototyping (1–2 weeks): gather requirements, create MVP specification.
- Design (2–3 weeks): UI/UX for iOS and Android, adaptation for dark theme and accessibility.
- Development (4–8 weeks for basic version): backend (Firebase/Supabase), offline maps, forecast, logbook.
- ML and AR integration (additional 3–4 weeks): train model, implement AR measurement.
- Testing (1–2 weeks): QA on real devices, offline mode testing.
- Publication (1 week): submit to App Store and Google Play, resolve potential rejections.
What's Included
- Requirements analysis and interface prototype.
- Backend development and integration with weather services.
- Offline maps with fishing spots.
- Bite forecast with ML module.
- Catch logbook with photo and AR measurement.
- Content moderation and rating system.
- Publication in App Store and Google Play.
- Technical support for 12 months after release.
- Guaranteed on-time delivery and competitive pricing.
Timeline Estimates and Cost
Basic version (offline map, catch logbook, bite forecast) — 6–10 weeks, starting at $15,000. Full version with ML identification, AR measurement, and community — 4–6 months, custom-quoted based on requirements. Our basic package starts at $15,000 and you save up to 30% (up to $4,500) compared to building from scratch using our reusable components. We offer a 12-month guarantee on all deliverables. If you want to discuss your app, contact us — we will prepare a commercial proposal.