The crop-scope's intro from zfranx

# Crop Scope A Flutter app that detects a plant's disease given a photo of an affected part of the plant. ## Visuals

## Installation A Flutter installation is required to run this project. To install Flutter, visit the official installation [documentation](https://docs.flutter.dev/get-started/install). Set up an editor of choice as specified [here](https://docs.flutter.dev/get-started/editor). Download the project. ```bash git clone https://github.com/root458/Crop-Scope.git ``` Run the below command inside the project directory to install necessary packages. ```bash flutter pub get ``` To run the project in debug mode ```bash flutter run ``` To generate a release build ```bash flutter build apk ``` Locate the `app-release.apk` file from the directory `build/app/outputs/flutter-apk/` and install in your Android smartphone or emulator to use. ## Usage On launching the application, you will be presented with the usage instructons. It follows that to get a suggestion of a disease affecting a plant of interest, take a photo of the plant, or select a photo of the plant from your `gallery`. The application then runs the TFLITE model in the background to get a suggestion of the disease. It displays the results on the next screen `Suggestions` ## Important to note - The `tflite` model has been trained to detect only a subset of the diseases. They include: - Pepper Bell Bacterial Spot - Pepper Bell Healthy - Potato Early Blight - Potato Healthy - Potato Late Blight - Tomato Bacterial Spot - Tomato Early Blight - Tomato Healthy - Tomato Late Blight - Tomato Leaf Mold - Tomato Septoria Leaf Spot - Tomato Spotted Spider Mites - Tomato Target Spot - Tomato Mosaic Virus - Tomato Yellow Leaf Curl Virus - The size of the dataset was only sufficient enough to make the model recognize selected diseases, but it faces problems with images of non-plants. - The application was built using Flutter and a `tflite` model from [Teachable Machine Learning by Google](https://teachablemachine.withgoogle.com/). The dataset was from [KAGGLE](https://www.kaggle.com/saroz014/plant-diseases). ## Contributing Contributions towards the project are welcome. Specifically: - The `tflite` model used can be replaced with a more accurate one/one with more diseases. - The responsiveness of the application can be improved. - Warning messages can be added for non-plant inputs made by users. ## License [MIT](https://github.com/root458/Suis-Flutter-Puzzle-Hack/blob/main/LICENSE) ## Project Status The requirements I set have been made possible. However, the application can still be improved. Additions/improvements can be made as specified in the `Contributing` section. # Crop-Scope

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crop-scope's Introduction

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