A CNN-based Flutter Mobile Application for disease detection in Tomato Plants. The application will provide farmers with an easy-to-use tool to identify and manage diseases, enabling early intervention and improved crop management practices. – Technology Used: Deep Learning, Convolutional Neural Networks(CNN), Flutter, Firebase
Tools Used in this projects are:
Flutter SDK: It provides a comprehensive set of development tools, frameworks, and libraries for building mobile apps that can run on both iOS and Android platforms.
Android Studio: A fully-featured IDE specifically designed for Android app development, which also includes robust Flutter support.
Convolutional Neural Network (CNN) Libraries: TensorFlow libraries for building and training CNN models, such as Keras.
TensorFlow Lite: TensorFlow Lite is a lightweight framework for deploying machine learning models on mobile and embedded devices.
Testing Tools: Various testing tools and frameworks are available for testing the functionality, performance, and user experience of your mobile application. These include Flutter's built-in testing framework, unit testing frameworks (e.g., Mockito), and automation tools (e.g., Flutter Driver or Appium) for UI testing