Overview

This guide details the comprehensive integration of AI-powered crop disease detection and data warehousing capabilities for the Bazaar platform, specifically designed for Kenyan farmers and agricultural stakeholders.

Table of Contents

1. System Architecture
2. AI Integration Components
3. Data Warehousing Flow
4. Setup Instructions
5. API Documentation
6. Data Models
7. Analytics & Reporting
8. Monitoring & Maintenance

System Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Frontend      │    │   Supabase       │    │   External      │
│   React App     │◄──►│   Edge Functions │◄──►│   Services      │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                │                         │
                                ▼                         │
                       ┌──────────────────┐              │
                       │   Supabase       │              │
                       │   Database       │              │
                       └──────────────────┘              │
                                │                         │
                                ▼                         │
                       ┌──────────────────┐              │
                       │   Data Warehouse │◄─────────────┘
                       │   (Azure/AWS/GCP)│
                       └──────────────────┘

AI Integration Components

1. OpenAI Vision Integration

Purpose: Real-time crop disease detection using GPT-4o vision capabilities

Flow:

1. User uploads crop image through React frontend
2. Image stored in Supabase Storage
3. Edge function calls OpenAI Vision API with specialized agricultural prompt