Krushi Mitra

Technical Architecture & Design Document

1. Overall Project Details

Krushi Mitra is an end-to-end Agritech ecosystem designed to modernize traditional farming through IoT, Data Science, and collaborative economy principles. The platform integrates soil health sensors with AI models to provide personalized crop recommendations, while simultaneously offering a peer-to-peer marketplace for farming equipment (tractors, tillers) and agricultural land rental. By centralizing data from the field and the market, Krushi Mitra helps farmers maximize yield and reduce operational costs.

2. Target Audience

• Modern Farmers: Seeking data-driven insights to improve soil health and crop productivity.
• Land & Equipment Owners: Looking to monetize idle assets through a secure rental platform.
• Agricultural Consultants: Needing accurate field data to provide better advice to their clients.

3. User Experience & Workflow

The app is built to be accessible and intuitive, focusing on three core pillars: Insights, Rental, and Marketplace.

User Journey Flowchart

4. Technical Architecture Flow

Krushi Mitra utilizes a hybrid cloud architecture to handle high-velocity IoT data streams and complex marketplace transactions.

System Architecture

5. Developer Role & Implementation Focus

• IoT Stream Processing: Implementing a high-throughput MQTT gateway and InfluxDB integration to handle thousands of soil data points per second.
• AI Recommendation Engine: Developing Python-based models that correlate soil pH, moisture, and NPK levels with local weather forecasts for accurate crop suggestions.
• Marketplace Escrow System: Building a secure payment flow that holds rental fees until both parties confirm a successful asset return.
• Responsive Data Viz: Using Chart.js to create easy-to-understand soil health trends for non-technical users.

6. Technology Stack & Tools Used

Frontend & Mobile:

• Core: Next.js (PWA), TypeScript, Tailwind CSS
• Visuals: Chart.js, Framer Motion
• Maps: Google Maps API (for land/tool location)

Backend & IoT:

• Runtime: Node.js (Express), Python (FastAPI)
• Protocol: MQTT (for IoT communication)
• Databases: MongoDB, InfluxDB (Time-series), Redis

Integrations:

• Payments: Stripe
• Weather: OpenWeather API
• Storage: Cloudinary (for equipment images)

7. Communication Structure (IoT to Insight)

The system ensures that sensor data is transformed into actionable advice within seconds.

IoT Data & AI Recommendation Flow