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https://github.com/codernayeem/agro-care-app

An shopping & community app for farmers, gardeners & plant enthusiasts made with flutter
https://github.com/codernayeem/agro-care-app

agriculture android community disease-detection firebase flutter flutter-android machine-learning marketplace news openweather social-network

Last synced: 11 months ago
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An shopping & community app for farmers, gardeners & plant enthusiasts made with flutter

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README

          

# agro-care-app
# Agro Care App

A plant disease detection, shopping, and community app for farmers, gardeners, and plant enthusiasts.

## Features
- Simple, clean, and easy app interface 👍
- Email-password and Google authentication
- Plant disease detection through leaf scanning (currently supports Tomato, Potato, Corn)
- Assistance via phone call or WhatsApp for farming and gardening support
- Weather updates including temperature and sky status
- Latest trends and news in the agriculture sector
- Fully functional marketplace for seeds, pesticides, irrigation supplies, indoor plants, gardening tools, fertilizers, etc., with cart, ordering, and payment integration via SSL Commerz
- Minimal social features including posting, liking, and commenting
- Profile page with user information display, edit profile functionality, profile picture upload, and view user's orders

## API & Services Used
- `FlutterFire`
* Ensure to login to Firebase from CLI
* Use FlutterFire to connect to Firebase (_generates a Dart file - without it the app won't run_)
- Firebase `Auth` for authentication
- Firebase `CloudStore` for database
- Firebase `Storage` for storage service
- `OpenWeather` API for weather information
- `Agro Care Flask` API for plant disease detection: [agro-care-flask](https://github.com/codernayeem/agro-care-flask)
* Run the Flask app locally or host it
* Set the prediction API link to the Firebase CloudStore
* Run the app and scan any leaf
* _Note: Without starting the server, the detection won't work. Currently, the server is hosted locally, so detection might not work._

## Project Requirements
- `Flutter` v3.16.9 or higher (_The project was developed on `v3.16.9`_)

## Usage

1. **Clone the repository:**
```bash
git clone https://github.com/codernayeem/agro-care-app.git
```
2. **Navigate to the project directory:**
```bash
cd agro-care-app
```
3. **Install dependencies:**
```bash
flutter pub get
```
4. **Configure FlutterFire:**
```bash
flutterfire configure
```
5. **Run the app:**
```bash
flutter run
```

## Test the app
- Download the latest release from [GitHub Releases](https://github.com/codernayeem/agro-care-app/releases)
- Install the APK on your device (_As the app is not uploaded to the Play Store, you might get a warning. You can safely ingore that._)
- Open the app and explore its features
- Regarding The Disease Detection through leaf scanning
* The server for detection api is not hosted in remote server yet.
* Since, the detection server [agro-care-flask](https://github.com/codernayeem/agro-care-flask) can be hosted locally, the local api needs to be set in the cloudstore everytime it changes.
* Currently, the api saved in cloud store is `http://192.168.0.103:4000/predict`
* You can run the server locally on that local IP address & port. Then, the detection in app should work fine.
* Currently, the model is not that robust yet, as the dataset was not that versatile. It is just for testing purpose of the pipeline.

## Screenshots

| ![Image 1](README_images/1.jpg) | ![Image 2](README_images/2.jpg) | ![Image 3](README_images/3.jpg) |
|---------------------------------|---------------------------------|---------------------------------|
| ![Image 4](README_images/4.jpg) | ![Image 5](README_images/5.jpg) | ![Image 6](README_images/6.jpg) |
| ![Image 7](README_images/7.jpg) | ![Image 8](README_images/8.jpg) | ![Image 9](README_images/9.jpg) |
| ![Image 10](README_images/10.jpg) | ![Image 11](README_images/11.jpg) | ![Image 12](README_images/12.jpg) |
| ![Image 13](README_images/13.jpg) | ![Image 14](README_images/14.jpg) | ![Image 15](README_images/15.jpg) |
| ![Image 16](README_images/16.jpg) | ![Image 17](README_images/17.jpg) | ![Image 18](README_images/18.jpg) |
| ![Image 19](README_images/19.jpg) | ![Image 20](README_images/20.jpg) | ![Image 21](README_images/21.jpg) |
| ![Image 22](README_images/22.jpg) | ![Image 23](README_images/23.jpg) | ![Image 24](README_images/24.jpg) |
| ![Image 25](README_images/25.jpg) | ![Image 26](README_images/26.jpg) | ![Image 27](README_images/27.jpg) |
| ![Image 28](README_images/28.jpg) | ![Image 29](README_images/29.jpg) | ![Image 30](README_images/30.jpg) |
| ![Image 31](README_images/31.jpg) | ![Image 32](README_images/32.jpg) | ![Image 33](README_images/33.jpg) |

### Credits
Various images, news information, marketplace details, and disease descriptions used in the app were sourced from the internet for educational purposes only.