IQACS — Intelligent Quality & Air Control System for Coffee Nursery Greenhouse

Repository Description (for GitHub): A Flutter-based mobile application for smart coffee nursery greenhouse management. IQACS integrates real-time IoT sensor monitoring (temperature & humidity), automated sprayer control, AI-powered coffee leaf disease detection using image classification, and data analytics with interactive charts and Excel export — all in one unified platform.

Overview

IQACS (Intelligent Quality & Air Control System) is a Flutter mobile application designed for smart management of coffee nursery greenhouses. The system connects to IoT hardware sensors that monitor temperature and humidity inside the greenhouse in real-time, allows operators to remotely control a water sprayer pump, and leverages AI/machine learning to detect coffee leaf diseases from photos taken directly in the field.

The application communicates with a backend REST API server and is built with a responsive layout supporting screens from mobile to 4K displays.

Key Features

Feature	Description
🌡️ Real-time Sensor Monitoring	Live temperature and humidity readings from IoT sensors installed in the greenhouse
💧 Remote Sprayer Control	Toggle the water sprayer pump on/off directly from the app
🤖 AI Leaf Disease Detection	Upload or capture a photo of a coffee leaf; the AI model classifies the disease and returns a diagnosis with an accuracy score
📊 Analytics & Charts	Line chart visualization of historical temperature and humidity data, filterable by custom date range (max 7 days)
📤 Excel Export	Export sensor chart data to `.xlsx` format for offline analysis
🌤️ Weather Integration	Displays current local weather conditions via OpenWeather API
📜 Diagnosis History	View, filter, and delete past diagnosis records categorized by disease type (Miner, Phoma, Rust, No Disease)
🔒 Authentication	Secure login with JWT Bearer token, OTP-based forgot password flow, and change password
👤 User Profile	View and edit profile, change profile photo
📱 Responsive Layout	Supports Mobile, Tablet, Laptop, and 4K screen breakpoints

Application Flow

1. Splash Screen → Onboarding → Login

The app starts with a Splash Screen that checks for an existing session token in SharedPreferences.
First-time users are shown an Onboarding Screen (3 slides).
Users authenticate via the Login Screen using email and password. On success, a JWT token and user ID are stored locally.

2. Main Dashboard (Home Screen)

After login, the user lands on the Home Screen which displays:

Custom AppBar — shows user greeting and weather info.
Main Card — shows live temperature and humidity values fetched from the IoT sensor device.
Filter Card — lets the user filter sensor data by time range.
Sensor Card — detailed temperature/humidity display card.
Sprayer Card — button to toggle the sprayer pump on/off via API.
Report Card — quick link to the analytics/report section.

3. Scanner Screen (Disease Detection)

The Scanner Screen provides two ways to submit a leaf photo for AI diagnosis:

Open Camera — captures a photo live from the device camera.
Open Folder — picks an image from the device gallery.

On submission, the image is uploaded via multipart POST to the backend AI inference endpoint. The API returns a diagnosis label (e.g., Miner, Phoma, Rust, No Disease) along with an accuracy percentage and an AI-generated description.

Result is displayed on the Predict Result Screen:

Full-width leaf image
Diagnosis name + accuracy badge
AI-generated markdown description with typewriter animation

The scanner screen also shows the Latest Diagnoses list (last hour), where each entry can be tapped to open the detail screen or deleted.

4. Analytic Screen (History)

Displays all previous diagnosis records in a masonry grid layout, filterable by disease category via tabs:

All | Miner | Phoma | No Disease | Rust

Tapping any item opens the Detail Analytic Screen showing the full diagnosis detail for that record.

5. Report Screen (Charts & Data)

Line Chart — visualizes average daily temperature and humidity over a selected date range (up to 7 days).
Humidity Detail — doughnut/radial chart per day.
Temperature Detail — doughnut/radial chart per day.
Export — exports the visible data to an Excel (.xlsx) file saved locally.
Filter — date range picker to select a custom period.

6. Profile & Account Management

View and edit user profile (name, email, phone).
Change profile photo.
Change password.
Forgot password flow: enter email → receive OTP → verify OTP → set new password.

Tech Stack

Framework: Flutter (Dart)
State Management: Riverpod (flutter_riverpod)
HTTP Client: Dio (dio) + http
Local Storage: SharedPreferences
Charts: fl_chart
AI Result Rendering: gpt_markdown, animated_text_kit
Image Handling: image_picker, camera
Export: excel, open_file
UI Utilities: google_fonts, gap, shimmer, flutter_animate, responsive_framework, flutter_staggered_grid_view
Notifications: awesome_snackbar_content, quickalert
Internationalization: intl, timeago

Project Structure

lib/
├── main.dart                     # App entry point, responsive breakpoints
├── constants/
│   ├── api_constant.dart         # All API base URLs and endpoints
│   └── dio_constant.dart         # Dio client configuration
├── models/
│   ├── model_user.dart           # Auth user model
│   ├── model_pengguna.dart       # User profile model
│   ├── model_alat.dart           # IoT device/sensor model
│   ├── model_chart.dart          # Chart data model
│   ├── model_data_predict.dart   # Diagnosis result models
│   ├── model_diagnosa.dart       # Diagnosa response model
│   ├── model_weather.dart        # Weather API model
│   ├── model_monicontrollings.dart
│   ├── model_forgot_password.dart
│   └── model_update_profile.dart
├── providers/
│   ├── login_provider.dart       # Auth state & login logic
│   ├── user_provider.dart        # User profile state
│   ├── diagnosa_provider.dart    # AI predict, list, delete
│   ├── chart_provider.dart       # Chart data + Excel export
│   ├── weather_provider.dart     # Weather fetch
│   ├── get_temp_humidity_provider.dart
│   ├── filter_sensor_provider.dart
│   ├── forgot_password_provider.dart
│   ├── check_otp_provider.dart
│   ├── change_password_provider.dart
│   ├── profile_provider.dart
│   ├── sharedpreferences_provider.dart
│   ├── page_provider.dart
│   ├── input_provider.dart
│   └── counter_provider.dart
├── screens/
│   ├── splash_screen.dart        # Splash + session check
│   ├── onboarding_screen.dart    # Onboarding slides
│   ├── login_screen.dart         # Login form
│   ├── forgot_password_screen.dart
│   ├── otp_verification_screen.dart
│   ├── reset_password.dart
│   ├── page_screen.dart          # Bottom nav container
│   ├── home_screen.dart          # Dashboard
│   ├── scanner_screen.dart       # Leaf disease scanner
│   ├── predict_result_screen.dart
│   ├── analytic_screen.dart      # Diagnosis history grid
│   ├── detail_analytic_screen.dart
│   ├── report_screen.dart        # Charts & export
│   ├── profile_screen.dart
│   ├── edit_profile_screen.dart
│   └── change_password.dart
├── widgets/
│   ├── custom_appbar.dart        # Top bar with weather
│   ├── custom_button_nav.dart    # Bottom navigation bar
│   ├── custom_card.dart          # Sensor, sprayer, report cards
│   ├── custom_filter.dart        # Sensor filter widget
│   └── custom_input.dart         # Reusable text input
└── functions/
    ├── dialog_func.dart
    ├── notification_func.dart
    ├── shimmer_card.dart
    └── snackbar_func.dart

Dependencies

Package	Version	Purpose
`flutter_riverpod`	^3.2.1	State management
`dio`	^5.7.0	HTTP client
`http`	^1.2.2	Additional HTTP
`shared_preferences`	^2.0.0	Token & session storage
`fl_chart`	^1.1.1	Line & pie charts
`image_picker`	^1.1.2	Gallery/camera image picker
`camera`	^0.11.0+2	Camera access
`excel`	^4.0.6	Excel file generation
`open_file`	^3.5.10	Open files natively
`gpt_markdown`	^1.1.5	Render markdown AI output
`animated_text_kit`	^4.2.2	Typewriter animation
`google_fonts`	^8.0.2	Typography
`shimmer`	^3.0.0	Loading skeleton effect
`responsive_framework`	^1.5.1	Responsive breakpoints
`flutter_staggered_grid_view`	^0.7.0	Masonry grid layout
`flutter_animate`	^4.5.0	UI animations
`intl`	^0.20.2	Date/number formatting
`timeago`	^3.7.0	Relative timestamps
`permission_handler`	^12.0.1	Runtime permissions
`encrypt`	^5.0.3	Data encryption
`quickalert`	^1.1.0	Alert dialogs
`awesome_snackbar_content`	^0.1.4	Styled snackbars
`logger`	^2.4.0	Debug logging

API Endpoints

All endpoints use the base URL configured in lib/constants/api_constant.dart.

Method	Endpoint	Description
`POST`	`/api/login`	User login
`POST`	`/api/logout`	User logout
`GET`	`/api/check-token`	Validate JWT token
`GET`	`/api/getdataalat/{id}`	Get live sensor data
`POST`	`/api/aturpompa`	Toggle sprayer pump
`GET`	`/api/chartdaritanggal/{range}`	Get chart data by date range
`POST`	`/api/diagnosa/{userId}`	Submit leaf image for AI diagnosis
`GET`	`/api/data-diagnosa/{filter}`	Get diagnosis history (filtered)
`GET`	`/api/data-diagnosa-detail/{id}`	Get single diagnosis detail
`DELETE`	`/api/data-diagnosa/{id}`	Delete a diagnosis record
`GET`	`/api/get-pengguna/{id}`	Get user profile
`POST`	`/api/updatefoto/{id}`	Update profile photo
`POST`	`/api/update-data-pengguna-without-photo/{id}`	Update profile data
`POST`	`/api/change-password/{id}`	Change password
`POST`	`/api/lupa-password`	Forgot password (send OTP)
`POST`	`/api/lupa-password/verifikasi-otp/{email}`	Verify OTP
`POST`	`/api/lupa-password/kirim-ulang-otp/{email}`	Resend OTP
`POST`	`/api/lupa-password/reset-password/{email}`	Reset password

Getting Started

Prerequisites

Flutter SDK ^3.5.2
Dart SDK ^3.5.2
Android Studio / VS Code with Flutter extension
A running backend API server

Installation

# Clone the repository
git clone https://github.com/agungkurniawanid/kopi-greenhouse-aircontrol-app.git
cd kopi-greenhouse-aircontrol-app

# Install dependencies
flutter pub get

Configuration

Edit lib/constants/api_constant.dart and update:

static const String baseUrl = 'http://YOUR_SERVER_IP:PORT';
static const String openWeatherApiKey = 'YOUR_OPENWEATHER_API_KEY';

Run the App

flutter run

Build Guide

# Generate launcher icons
flutter pub run flutter_launcher_icons

# Build APK (debug)
flutter build apk --debug

# Build APK (release)
flutter build apk --release

# Build App Bundle (Play Store)
flutter build appbundle --release

IQACS — Intelligent Quality & Air Control System untuk Greenhouse Pembibitan Kopi

Deskripsi Repository (untuk GitHub): Aplikasi mobile berbasis Flutter untuk manajemen greenhouse pembibitan kopi yang cerdas. IQACS mengintegrasikan pemantauan sensor IoT real-time (suhu & kelembapan), kontrol sprayer otomatis, deteksi penyakit daun kopi berbasis AI menggunakan klasifikasi gambar, serta analitik data dengan grafik interaktif dan ekspor Excel — semua dalam satu platform terintegrasi.

📋 Daftar Isi

Gambaran Umum
Fitur Utama
Alur Aplikasi
Teknologi yang Digunakan
Struktur Proyek
Daftar Dependensi
Endpoint API
Cara Memulai
Panduan Build

Gambaran Umum

IQACS (Intelligent Quality & Air Control System) adalah aplikasi mobile Flutter yang dirancang untuk manajemen greenhouse pembibitan kopi secara cerdas. Sistem ini terhubung ke perangkat sensor IoT yang memantau suhu dan kelembapan di dalam greenhouse secara real-time, memungkinkan operator mengontrol pompa sprayer air dari jarak jauh, serta memanfaatkan kecerdasan buatan (AI/ML) untuk mendeteksi penyakit daun kopi dari foto yang diambil langsung di lapangan.

Aplikasi berkomunikasi dengan server backend REST API dan dibangun dengan layout responsif yang mendukung berbagai ukuran layar dari ponsel hingga 4K.

Fitur Utama

Fitur	Deskripsi
🌡️ Monitoring Sensor Real-time	Pembacaan suhu dan kelembapan langsung dari sensor IoT di greenhouse
💧 Kontrol Sprayer Jarak Jauh	Nyalakan/matikan pompa sprayer air langsung dari aplikasi
🤖 Deteksi Penyakit Daun Kopi (AI)	Upload atau ambil foto daun kopi; model AI mengklasifikasikan penyakit dan memberikan diagnosis beserta skor akurasi
📊 Analitik & Grafik	Visualisasi grafik garis data suhu dan kelembapan historis, dapat difilter berdasarkan rentang tanggal (maks. 7 hari)
📤 Ekspor Excel	Ekspor data grafik sensor ke format `.xlsx` untuk analisis offline
🌤️ Integrasi Cuaca	Menampilkan kondisi cuaca lokal terkini melalui OpenWeather API
📜 Riwayat Diagnosis	Lihat, filter, dan hapus riwayat diagnosis yang dikategorikan berdasarkan jenis penyakit (Miner, Phoma, Rust, Tidak Berpenyakit)
🔒 Autentikasi	Login aman dengan JWT Bearer token, alur lupa password berbasis OTP, dan ganti password
👤 Profil Pengguna	Lihat dan edit profil, ganti foto profil
📱 Layout Responsif	Mendukung breakpoint Mobile, Tablet, Laptop, dan 4K

Alur Aplikasi

1. Splash Screen → Onboarding → Login

Aplikasi dimulai dengan Splash Screen yang mengecek token sesi yang tersimpan di SharedPreferences.
Pengguna baru akan ditampilkan Onboarding Screen (3 slide).
Pengguna melakukan autentikasi melalui Login Screen menggunakan email dan password. Jika berhasil, token JWT dan ID pengguna disimpan secara lokal.

2. Dashboard Utama (Home Screen)

Setelah login, pengguna masuk ke Home Screen yang menampilkan:

AppBar — menampilkan sapaan pengguna dan informasi cuaca.
Kartu Utama — menampilkan nilai suhu dan kelembapan langsung dari sensor IoT.
Kartu Filter — memungkinkan pengguna memfilter data sensor berdasarkan rentang waktu.
Kartu Sensor — tampilan detail suhu/kelembapan.
Kartu Sprayer — tombol untuk mengaktifkan/menonaktifkan pompa sprayer melalui API.
Kartu Laporan — tautan cepat ke halaman analitik/laporan.

3. Scanner Screen (Deteksi Penyakit)

Scanner Screen menyediakan dua cara untuk mengirim foto daun untuk diagnosis AI:

Buka Kamera — mengambil foto langsung dari kamera perangkat.
Buka Folder — memilih gambar dari galeri perangkat.

Setelah dikirim, gambar diunggah melalui multipart POST ke endpoint inferensi AI backend. API mengembalikan label diagnosis (contoh: Miner, Phoma, Rust, No Disease) beserta persentase akurasi dan deskripsi yang dihasilkan AI.

Hasil ditampilkan di Predict Result Screen:

Gambar daun lebar penuh
Nama diagnosis + badge akurasi
Deskripsi markdown yang dihasilkan AI dengan animasi typewriter

Scanner screen juga menampilkan daftar Diagnosis Terbaru (satu jam terakhir), setiap entri dapat diketuk untuk membuka halaman detail atau dihapus.

4. Analytic Screen (Riwayat)

Menampilkan semua riwayat diagnosis dalam layout masonry grid, dapat difilter berdasarkan kategori penyakit melalui tab:

Semua | Miner | Phoma | No Disease | Rust

Mengetuk item akan membuka Detail Analytic Screen yang menampilkan detail diagnosis lengkap untuk rekaman tersebut.

5. Report Screen (Grafik & Data)

Grafik Garis — memvisualisasikan rata-rata suhu dan kelembapan harian dalam rentang tanggal yang dipilih (maks. 7 hari).
Detail Kelembapan — grafik donat/radial per hari.
Detail Suhu — grafik donat/radial per hari.
Ekspor — mengekspor data yang terlihat ke file Excel (.xlsx) yang disimpan secara lokal.
Filter — date range picker untuk memilih periode kustom.

6. Profil & Manajemen Akun

Lihat dan edit profil pengguna (nama, email, nomor telepon).
Ganti foto profil.
Ganti password.
Alur lupa password: masukkan email → terima OTP → verifikasi OTP → set password baru.

Teknologi yang Digunakan

Framework: Flutter (Dart)
State Management: Riverpod (flutter_riverpod)
HTTP Client: Dio (dio) + http
Penyimpanan Lokal: SharedPreferences
Grafik: fl_chart
Render Hasil AI: gpt_markdown, animated_text_kit
Penanganan Gambar: image_picker, camera
Ekspor: excel, open_file
UI: google_fonts, gap, shimmer, flutter_animate, responsive_framework, flutter_staggered_grid_view
Notifikasi: awesome_snackbar_content, quickalert
Internasionalisasi: intl, timeago

Struktur Proyek

lib/
├── main.dart                     # Entry point aplikasi, breakpoint responsif
├── constants/
│   ├── api_constant.dart         # Semua base URL dan endpoint API
│   └── dio_constant.dart         # Konfigurasi Dio client
├── models/                       # Model data (JSON serialization)
├── providers/                    # State management Riverpod
├── screens/                      # Halaman-halaman aplikasi
├── widgets/                      # Komponen UI yang dapat digunakan ulang
└── functions/                    # Fungsi utilitas (snackbar, dialog, dll.)

Daftar Dependensi

Lihat bagian Dependencies di atas untuk daftar lengkap paket yang digunakan.

Endpoint API

Semua endpoint menggunakan base URL yang dikonfigurasi di lib/constants/api_constant.dart. Lihat bagian API Endpoints di atas untuk daftar lengkapnya.

Cara Memulai

Prasyarat

Flutter SDK ^3.5.2
Dart SDK ^3.5.2
Android Studio / VS Code dengan ekstensi Flutter
Server backend API yang sedang berjalan

Instalasi

# Clone repositori
git clone https://github.com/agungkurniawanid/kopi-greenhouse-aircontrol-app.git
cd kopi-greenhouse-aircontrol-app

# Install dependensi
flutter pub get

Konfigurasi

Edit lib/constants/api_constant.dart dan perbarui:

static const String baseUrl = 'http://IP_SERVER_ANDA:PORT';
static const String openWeatherApiKey = 'API_KEY_OPENWEATHER_ANDA';

Jalankan Aplikasi

flutter run

Panduan Build

# Generate launcher icons
flutter pub run flutter_launcher_icons

# Build APK (debug)
flutter build apk --debug

# Build APK (release)
flutter build apk --release

# Build App Bundle (Play Store)
flutter build appbundle --release

IQACS — Intelligent Quality & Air Control System | Sistem Pemantauan & Kontrol Kualitas Udara Greenhouse Kopi

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
android		android
assets		assets
ios		ios
lib		lib
linux		linux
macos		macos
web		web
windows		windows
.gitignore		.gitignore
.metadata		.metadata
README.md		README.md
analysis_options.yaml		analysis_options.yaml
pubspec.lock		pubspec.lock
pubspec.yaml		pubspec.yaml

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IQACS — Intelligent Quality & Air Control System for Coffee Nursery Greenhouse

📋 Table of Contents

Overview

Key Features

Application Flow

1. Splash Screen → Onboarding → Login

2. Main Dashboard (Home Screen)

3. Scanner Screen (Disease Detection)

4. Analytic Screen (History)

5. Report Screen (Charts & Data)

6. Profile & Account Management

Tech Stack

Project Structure

Dependencies

API Endpoints

Getting Started

Prerequisites

Installation

Configuration

Run the App

Build Guide

IQACS — Intelligent Quality & Air Control System untuk Greenhouse Pembibitan Kopi

📋 Daftar Isi

Gambaran Umum

Fitur Utama

Alur Aplikasi

1. Splash Screen → Onboarding → Login

2. Dashboard Utama (Home Screen)

3. Scanner Screen (Deteksi Penyakit)

4. Analytic Screen (Riwayat)

5. Report Screen (Grafik & Data)

6. Profil & Manajemen Akun

Teknologi yang Digunakan

Struktur Proyek

Daftar Dependensi

Endpoint API

Cara Memulai

Prasyarat

Instalasi

Konfigurasi

Jalankan Aplikasi

Panduan Build

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