AgriTech Documentation

Welcome to the official documentation for AgriTech. This page provides a brief overview. For detailed architecture and setup guides, please consult the repository documentation.

Need more details? Check out the comprehensive Project Documentation in our repository.

Open Source

This project is fully open source. Models are pre-trained and available in the repo.

Installation & Setup

Docker Setup (Recommended)

Run the entire stack (Frontend + Backend + DB) with a single command.

bash

git clone https://github.com/nishanth-kj/agriculture.git

cd agriculture

# Start services

docker-compose up --build

Manual Setup

Backend (Python)

  • Python 3.12+ Required
  • Uses uv for package management
  • cd api && uv sync
  • python manage.py runserver

Frontend (Next.js)

  • Node.js 20+ Required
  • cd web
  • npm run dev

Python Backend API

The backend runs on port 8000 by default.

POST/api/crop-yield/
Inference

Predicts crop yield using Random Forest Regressor.

Payload

{
  "nitrogen": 50,
  "phosphorus": 50,
  "potassium": 50,
  "temperature": 26,
  "humidity": 80,
  "ph": 7,
  "rainfall": 200
}
POST/api/prediction/pest-predict/
Computer Vision

Uses CNN to detect pests from uploaded images.

Payload

multipart/form-data
file: <image_file>

Contributing

We welcome contributions from the community! Whether it's fixing bugs, improving documentation, or proposing new features, your help is appreciated.

View Contribution Guide GitHub Repository