HyFish: AI-Powered Fisheries Mobile Application

Led the mobile development of HyFish, that uses machine learning to assess fish freshness and identify species, while also serving as an educational and community platform for the fisheries community.

• Links:
  • Github Repository
  • App Demo
  • Presentation

Overview

• Role: Mobile Developer
• Problem/Goal: The Indonesian fisheries sector lacked tools that are easily accessible to consumers and professionals to verify fish quality and share knowledge. Our goal was to develop an intuitive mobile application that provided fish scanning features, educational content, and a community forum to empower users and promote food safety.

Key Features

• Scanning Tools
  • Fish Freshness Detection
  • Fish Species Classification
• Educational Resources
  • Fish Encyclopedia
  • Educational Articles
• Community Forum
  • User Forum for Discussions
  • Toxic Content Filtering

Key Contributions

Responsible for the full mobile development lifecycle as a lead mobile developer for HyFish, including initial UI/UX design in Figma, implementing core features, and integrating the Machine Learning models. My key contributions included:

UI/UX Design

• Designed the entire application's user interface and experience from scratch using Figma.
• Created an intuitive, user-friendly, and visually appealing design, based on the android design guidelines.

Mobile Application Development

• Implemented the Figma designs into a fully functional and responsive Android application using Kotlin and Android Studio.
• Implemented all front-end features, including user authentication and sessions, core scanning features, history and database, and forum.
• Used Retrofit to seamlessly connect the Android app with the backend API, for features like fetching educational articles, displaying user-generated forum content, and retrieving fish details.

Tech Stack

• Design: Figma
• IDE: Android Studio with Kotlin
• Architecture & Jetpack: ViewModel, LiveData, Android KTX, DataStore, Room Persistence Library
• Networking: Retrofit
• Machine Learning: TensorFlow Lite
• Version Control: Git & GitHub

Results