Scan Your Plant and get Insights.
Explore the docs »
View Demo
·
Report Bug
·
Request Feature
Table of Contents
project screenshot is yet to be added
Find your diease and get insights about it. This project is under devlopment and part of Accenture hack and community service project.
Why this project?
- Farmers are not able to identify the disease of their plants.
- Farmers are not able to get the insights about the disease.
- Farmers are not able to get the solution of the disease.
This section should list any major frameworks/libraries used to bootstrap your project. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.
This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.
This is an example of how to list things you need to use the software and how to install them.
create .env file in assets/ folder and add the following api keys
flask_api= 'your_flask_api'
SENTRY_DSN = 'your_sentry_dsn'
OPEN_WEATHER_API_KEY = 'your_api_key'
APPWRITE_PROJECT_ID = 'your_project_id'
Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.
- Get your API Key at https://www.appwrite.com
- Clone the repo
git clone https://github.com/gopalkumr/farmhelp
- Install all dependencies
flutter pub get
Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.
For more examples, please refer to the Documentation
- [] Building the Authentication System
- [] designing the UI
- [] Training the model
- Deploying the model
See the open issues for a full list of proposed features (and known issues).
see the trained Model. Click here
We used convolution networks for image classification of the disease classes. We converted the model and optimized it using the tensorflowlite format to be used on the android application in memory and time-efficient manner. The tensorflowlite converts the large heavy deep learning models to a smaller and mobile hardware supportive format. It also quantizes the parametric learning weights to reduce the model file size. For example, we converted our convolution model file of 2mb to 200kbs without compromising on the performance of the model. All the database for this app is stored locally to avoid the requirement of internet connection for its usage. The user just needs to click the image of his plant and the app helps them out with the rest.
None Of the flutter dependencies seems to working fine eg. Tflite (depreciated), tflite_flutter, flutter_tflite. We are trying our best to embed the pretrained model with flutter.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Gopal Kumar - @gopalkumar0 - [email protected]
Project Link: https://github.com/gopalkumr/farmhelp
Gopal Kumar - @gopalkumar0 - Swetha PR - @swethaparthiban04 - Sachin Singh @sachin-singh-57a474206 -
This project is under devlopment and part of Accenture hack and community service project.