An AI-driven android app that helps farmers in yield optimization and crop diversification. It was developed during the Google's Hack4Change hackathon organized at ISB Hyderabad, India.

• Flutter: Android App Development
• FastAPI (Python): Backend API Development
• SQLModel (ORM): Database Ops
• Scikit-Learn: Machine Learning Models
• Python: Prototyping ML Pipelines
• ARCore: AR Features

Jaivardhan Shukla: Spearheading the design of the Flutter app, implementing augmented reality (AR) via ARCore, developing and deploying machine learning (ML) models, and seamlessly integrating APIs into the application.

Soham Ghughare: Proficiently managing database operations, prototyping ML pipelines, and contributing to API development.

Sushant Sourav: Crafting the UI/UX design of the application using Figma, ensuring a seamless and intuitive user experience.

Mosin Shaikh: Conducting extensive business research and contributing to the development of a compelling pitch for the hackathon.

Rahul Khatri: Engaging in thorough business research and collaborating on the development of a compelling pitch for the hackathon.

1. Login and Sign-Up:

• Used for user authentication and registration

2. Dashboard:

• This screen acts as a comprehensive display showcasing all farm entries created by a specific user.
• Users can conveniently access their existing farms using the intuitive interface.
• Seamlessly create new farm entries with ease through this user-friendly platform.

3. Add new Entry:

• Effortlessly add a new farm entry through this user-friendly option.
• The API automatically fetches essential geo-data and details on soil and land characteristics when the user initiates a new entry, eliminating the need for manual input.
• Streamlined usability for farmers as all requirements for manual input are truncated.
• Upon completion of initial data fetching, users are prompted to employ the AR-input feature.
• Users can move over their farm's perimeter using AR-input for the app to record and subsequently calculate the shape and area details of the farmland.
• The data fetching process for the new farm entry is efficiently completed through this comprehensive workflow.

4. Prediction and Suggestions screen:

• Ingenious Crop Prediction: The system predicts the most financially rewarding crops for the farm by considering the current market value of crop products.
• Comprehensive Land Assessment: It evaluates the land conditions to enhance the accuracy of crop recommendations.
• Sustainable Practices Forecast: The system anticipates and recommends sustainable practices crucial for maintaining the long-term productivity of the crops.
• Diversification Guidance: It suggests practices aimed at increasing crop diversification, contributing to a more resilient and varied agricultural portfolio.
• Holistic Agricultural Planning: The system offers a comprehensive approach to agricultural planning, considering both economic and environmental factors for optimal results.

For any inquiries related to the project or to learn more about me, please don't hesitate to reach out on LinkedIn 🚀. I look forward to connecting with you! 🌐