🌱SMART AGRICULTURE

For detailed system architecture refer to docs folder

Project Overview

This is a modern monitoring system for Agriculture leveraging modern web, AI, ML, and backend technologies to monitor, analyze, and automate agriculture operations.

It is built using a full-stack, cloud-native approach with technologies like FastAPI, Next.js, Docker, Supabase, and more.

Smart farm is a web-based livestock management system that helps dairy farmers digitize animal health, reproduction, and farm record-keeping.

The platform enables farmers to track breeding cycles, health events, treatments, and expenses to improve productivity and reduce preventable losses.

Feature

• 🌱 Smart farm data collection and visualization
• 📡 API endpoints for future advancementments to sensors and drones
• 📊 Real-time dashboards and analytics
• 🔐 Secure authentication and role-based access
• 🔁 CI/CD for automated testing and deployment
• 🌐 Scalable infrastructure using Docker and cloud platforms

Tech Stack

Backend

• python 3.9 and above
• FastAPI - High-performance API
• Uvicorn - ASGI server
• Node.js - backend components
• Alembic - handling db migrations
• Gunicorn - WSGI HTTP server for production
• SQLAlchemy - ORM for database operations
• Pydantic - Data validation and settings management
• Redis - Caching and real-time data handling

🌐 Frontend

• Next.js 14
• TypeScript
• Tailwind CSS – Utility-first CSS framework
• DaisyUI

🐳 Infrastructure

• Docker – Containerization
• NGINX – Reverse proxy, load balancer and frontend static file serving
• GitHub Actions – CI/CD automation
• Render - backend hosting
• supabase - postgre db
• Neon - Postgre db
• vercel - frontend hosting
• Firebase - frontend hosting
• service worker - for WPA

🛡️ Development Tools & Quality

• Black - Code formatting
• isort - Import statement organization
• Ruff - Extremely fast Python linter
• mypy - Static type checking
• Bandit - Security linter for Python
• pre-commit hooks - Automated code quality checks
• pytest - Testing framework
• Coverage.py - Code coverage analysis

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🛠️ Getting Started

Prerequisites

• Docker + Docker Compose
• Git
• Optional: Python (for local FastAPI dev), Node.js (for local frontend dev)
• Ensure you configure .env file as most services won't work without it

🔧 Local Development

# Clone the repository
git clone https://github.com/samnjoro30/Smart_Agriculture.git
cd Smart_Agriculture

Option 1: Run with Docker

  docker-compose up --build

option 2: Manual Development Setup for controlled development

Navigate to backend folder and activate the environment

# backend folder
  cd backend
# create virtual environment (if not already created)
  uv venv  --python 3.11
# activate environment
  source .venv/bin/activate
# install dependencies
  uv sync
# run backend server
  uvicorn API:app --reload

frontend server

# navigate to frontend structure
   cd frontend
   pnpm install
   pnpm run dev

running celery worker

   cd backend
# make sure redis is running before running celery worker
celery -A worker.celery_app worker \
  --loglevel=info \
  -Q livestock_service,default

Backend Infrastructure

Backend System Architecture

• for folder structure check out on docs/structure.md

DevOps ecosystem(backend)

Scurity features for backend

Bandit - f or security linting

Future Features

• 🌦️ Weather API integration

• 📍 GPS-based crop tracking

• 🤖 ML-based yield prediction

• 📲 Progressive Web App (PWA) support

• Mobile App built on flutter