Mobile App For Maize Plant Disease Detection

Abstract

1. Introduction

A Flutter-based mobile application for Maize Plant Disease Detection using Convolutional Neural Networks (CNN). This app provides a comprehensive solution for identifying maize plant diseases, their symptoms, treatments, generating PDF reports, and enabling user feedback to administrators.

2. Objective

A Flutter-based mobile application for Maize Plant Disease Detection using Convolutional Neural Networks (CNN). This app provides a comprehensive solution for identifying maize plant diseases, their symptoms, treatments, generating PDF reports, and enabling user feedback to administrators.

This project demonstrates how Dart, Flutter can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Firebase Signup & Sign-In: Securely register and log in with your email and password.
• Image Pick & Capture: Easily choose images from the gallery or capture them using the in-app camera.
• Disease Identification: Utilizes a CNN model to identify maize plant diseases and provide information about them.
• Symptoms and Treatments: Detailed information about the symptoms and suggested treatments for identified diseases.
• PDF Report Generation: Create PDF reports with disease images and relevant details.
• Feedback to Admin: Provide feedback to administrators to help improve the app.
• Contributor: [Your Name]
• API Deployment: The backend API for disease detection is deployed on Render.com. The API endpoints are accessible through this mobile app.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Flutter SDK
• Android Studio or VS Code with Flutter plugin
• Android / iOS device or emulator
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/uditmahato/mobile_app_for_maize_plant_disease_detection.git
cd mobile_app_for_maize_plant_disease_detection

1. Clone the Repository:
2. Firebase Configuration:
3. Create a new Firebase project on the Firebase Console.
4. Configure Firebase for your app and download the google-services.json file.
5. Place the google-services.json in the android/app directory.
6. Add the necessary Firebase SDK dependencies to your android/app/build.gradle and android/build.gradle files.
7. Flutter Dependencies:
8. Run the App:

git clone https://github.com/[your-username]/[your-repo-name].git
    cd [your-repo-name]

flutter pub get

flutter run

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by uditmahato and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.