Agriwise is a platform designed to assist farmers in the agricultural process, from planting to harvest. It leverages AI technology to provide valuable insights and recommendations to optimize crop yield and enhance farming efficiency.
- Soil Analysis: Determine soil quality and availability for farming based on comprehensive analysis.
- Crop Recommendation: Get personalized crop recommendations based on soil type, temperature, humidity, and season.
- Disease Detection: Detect plant diseases through image recognition for timely intervention.
- Community Platform: Join a vibrant community of farmers to share experiences and seek advice.
- Consulting Services: Connect with agricultural experts for paid consulting and personalized guidance.
- Kotlin: The primary programming language used for developing the Agriwise Android application.
- Android SDK: The Android Software Development Kit provides a set of tools and libraries to develop Android applications.
- Retrofit: Used for efficient networking and API communication with the backend server.
- Google Maps API: Integrated for location-based features and mapping functionality.
- Weather API: Weather API to retrieve weather data, such as temperature and humidity
- Language: app developed using the Kotlin. Kotlin is a modern, concise, and expressive language that is fully compatible with Java, making it a popular choice for Android development.
- IDE: Android Studio is the preferred IDE for developing Android applications. It provides a comprehensive set of tools, including code editors, debuggers, emulators, and more.
- Network Communication: Retrofit, a widely-used networking library, simplifies the process of making API calls, handling responses, and processing data in a type-safe manner.
- Weather Data: The Agriwise app integrates with a Weather API to provide accurate weather information for auto-filling and precise recommendations based on the user's location.
- Camera Integration: The app integrates the device's camera functionality to allow users to capture and upload photos. This feature is particularly useful for disease detection, as users can take pictures of affected plants and send them to the backend for analysis.
- Machine Learning Model: The backend of the Agriwise app includes a machine learning model for disease detection. The app sends the uploaded photos to the backend, where the model analyzes the images and provides insights on the presence of any diseases or abnormalities in the plants.
- User Interface: The app incorporates intuitive and user-friendly interfaces using Android's built-in UI components and layouts. It focuses on providing a seamless experience for farmers to access features such as soil analysis, crop recommendations, and community engagement.
Here are some screenshots from the Agriwise app:
*Onboarding Screen*
*Login/Register Screen*
Login Screen
Register Screen
Home Screen Showing all AI model Features
Profile Screen
Notifications Screen
Crop Safety model Screen
Crop recommendation model Screen
Soil classification model Screen
Soil fertilizer model Screen