Farmer Marketplace: Facilitating Buy and Sell Platform for 2M Farmers
- Industries : Dairy
- Domain : Agriculture
- Location : India
At a Glance
The client wanted to develop buy and sell platform for their authenticated farmers to provide them trusted way of selling their cattle avoiding publicly available such platform which caused loss to farmer due to fake Ad and other trust factors. Client also wanted to see the transaction happening to understand farmer situation.
A mobile app and web app were implemented with automated and manual verification of posted Ad along with providing trusted contact detail of farmer.
Web App / Hybrid Mobile app
Trusted Content and Connect
Data Analytics & Reporting
Challenges
- Limited Market Access: Difficulty in reaching trusted potential buyers and sellers within their region.
- Fraudulent Listings: Presence of fake ads and images reduced trust in existing platforms.
- Communication Barriers: Inefficient communication methods hindered negotiation and transaction processes.
- Technological Literacy: Limited familiarity with advanced technologies and platforms.
- Quality Assurance: Ensuring that listed products met quality standards without manual intervention.
- Data Management: Lack of a centralized system for managing farmer buy/sell transactions, and communications.
Objectives
- Connectivity: Enable farmers to connect with others in their region.
- Validation: Implement image analysis to ensure the authenticity of product images.
- Accessibility: Provide mobile app access for farmers and web-based administration for managers.
- Analytics: Use Apache Superset to create dashboards for monitoring platform activity.
- Cost Efficiency and Performance: Optimize image storage and retrieval costs and performance.
How we helped?
The buy and sell platform for farmers, implemented using Microsoft .NET Blazor, Microsoft SQL Server, Azure, Bunny.net and Apache Superset effectively addressed the challenges faced by both farmers and the dairy company. By improving connectivity, ensuring quality through image validation, and optimizing communication and data management, the platform enhanced the overall efficiency and trustworthiness of regional agricultural trade.
Features and Implementation
Farmer Mobile App
User Registration and Authentication: Utilize ASP.NET Identity for secure authentication.
Ad Posting: Farmers could post ads in their regional language with details including product name, quantity, price, and images. Basic form validations ensured all required fields were completed.
Image Upload and Analysis: When uploading images, a custom machine learning model integrated with Azure Cognitive Services analyzed the images to verify their content. This ensured only relevant images were uploaded. Images were stored in Azure Blob Storage.
Image Delivery Optimization: leverage Bunny.net to cache images, allowing faster retrieval for users and reducing Azure egress costs.
Chat Functionality: Integrated real-time chat using SignalR to facilitate communication between buyers and sellers.
Admin Web App
Dashboard: Created with Blazor, providing admins with a comprehensive view of platform activities. Key metrics included the number of active users, ads posted, and transactions completed.
Ad Approval Workflow: Admins could review image analysis results and approve or reject ads. This workflow included notifications and logging for audit purposes.
User Management: Admins could manage user accounts, including resetting passwords and deactivating or deleting accounts.
Backend Services
Ad Management: Handled CRUD operations for ads, including validation checks and business logic enforcement.
Image Analysis Service: Implemented as a microservice, this component received images, processed them using the ML model, and returned validation results to the main application.
Data Storage: SQL Server was used to store user data, ad details, and transaction records. Entity Framework Core was employed for ORM (Object-Relational Mapping).
Image Storage and Delivery
Azure Blob Storage: All images uploaded by users were stored in Azure Blob Storage, ensuring scalability and reliability.
Net Integration: Images stored in Azure were served through Bunny.net. This setup allowed caching of images, resulting in faster retrieval times for users and reduced costs associated with Azure egress.
Graphical Dashboards
Data Aggregation: Data from SQL Server was periodically aggregated and fed into Apache Superset for visualization.
Custom Dashboards: Created to track metrics such as user engagement, ad performance, and geographic distribution of users and transactions.
Alerts and Reports: Configured to send alerts for specific conditions (e.g., unusual spike in ad postings) and generate periodic reports for stakeholders.
