Empowering Farmers: AI-Powered Agri Tech Platform with Microfinance and Project Monitoring

Empowering Farmers: AI-Powered Agri Tech Platform with Microfinance and Project Monitoring

Project Summary

The Agri Tech Platform with Microfinance and Project Monitoring is a comprehensive system developed for a leading agricultural cooperative to empower small-scale farmers. Built with .NET Core for robust backend processing, Blazor for web interfaces, and .NET MAUI for cross-platform mobile apps, it integrates AI for crop yield predictions and risk assessments. Deployed on Azure with Docker for scalability, the platform enables farmers to access microloans, monitor crop projects in real time, and receive AI-driven recommendations. Supporting over 10,000 farmers, it streamlines financial services, project tracking, and agricultural insights for improved yields and economic stability.

Key Challenges Addressed

  • Access to Microfinance for Small Farmers: Providing secure, accessible microloan services in remote areas with limited banking infrastructure.
  • Real-Time Project Monitoring in Agriculture: Tracking crop growth, soil health, and project progress across diverse farmlands with accurate, timely data.
  • Scalable AI Integration for Predictive Insights: Delivering personalized AI recommendations for crop management while ensuring performance on web and mobile platforms.

Solutions Implemented

To enable microfinance access, we developed secure loan application and approval workflows using .NET Core and Azure Functions, integrating with payment gateways for seamless disbursements and repayments via the .NET MAUI mobile app. This ensured farmers in remote areas could apply for and manage microloans directly from their devices. For real-time project monitoring, we incorporated IoT sensors with .NET IoT libraries to collect soil and weather data, visualized through Blazor-based web dashboards and the mobile app, providing farmers with instant updates on project status. To integrate scalable AI, we built predictive models using ML.NET for crop yield forecasts and risk assessments, deployed on Azure Machine Learning, with results delivered through personalized notifications on the mobile app and web portal, supported by Dockerized microservices for efficient scaling.

Technology Stack

  • Backend: .NET Core, C#
  • Frontend (Web): Blazor, Bootstrap
  • Mobile: .NET MAUI, iOS, Android
  • AI: ML.NET
  • Database: Azure SQL Database
  • Cloud & Deployment: Azure, Docker
  • IoT: .NET IoT libraries
  • APIs: RESTful APIs
  • Tools: Visual Studio, Azure DevOps, Git

Client Benefits

  • Improved Financial Access: Enabled 40% faster microloan approvals, increasing farmer funding availability and economic empowerment.
  • Enhanced Project Efficiency: Reduced crop losses by 25% through real-time monitoring and AI-driven recommendations.
  • Scalable Operations: Azure and Docker supported seamless handling of growing user bases, with 99.9% uptime for web and mobile services.

Approach

  • Secure Microfinance Workflows
    Built .NET Core backend with Azure Functions for loan processing, integrated with .NET MAUI for mobile access and secure authentication.
  • IoT-Enabled Monitoring
    Deployed .NET IoT for sensor data collection, visualized via Blazor web interfaces and mobile app for real-time project insights.
  • AI-Driven Predictions
    Developed ML.NET models for yield predictions, hosted on Azure with Docker for scalability and seamless integration with web and mobile fronts.
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