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aryanb10/smart_agrihub

Analytics & monitoring
1 stars Quality 35 Trend 13
Repository Install

The Smart AgriHub is a technology-based platform that helps farmers improve crop productivity using data like weather and soil conditions.

Overview

Full-stack Agriculture AI Application with Crop Prediction, Fertilizer Recommendation, and Plant Disease Detection. - : Suggests the best crop based on soil NPK and weather data. - : Recommends fertilizer for specific soil and crop types. - : Upload plant leaf images to detect diseases (Simulated AI for MVP). - : View history and analytics. - : Powered by Replit Auth. - : React, Vite, TailwindCSS, Shadcn UI - : Node.js, Express - : Python (Scikit-Learn, NumPy) - : PostgreSQL (Drizzle ORM) 1. Install dependencies: 2. Start the development server: 3. Open http://localhost:5000 The Python scripts in server/ml/ train simple models on-the-fly for demonstration. For production, replace the mock training data with real datasets and save the models using joblib or pickle. - : Can be deployed to Vercel (see vercel.json). - : Can be deployed to Render or Heroku.

README

SMART-AGRI-HUB

Full-stack Agriculture AI Application with Crop Prediction, Fertilizer Recommendation, and Plant Disease Detection.

Features

  • Crop Prediction: Suggests the best crop based on soil NPK and weather data.
  • Fertilizer Recommendation: Recommends fertilizer for specific soil and crop types.
  • Disease Detection: Upload plant leaf images to detect diseases (Simulated AI for MVP).
  • Dashboard: View history and analytics.
  • Secure Auth: Powered by Replit Auth.

Tech Stack

  • Frontend: React, Vite, TailwindCSS, Shadcn UI
  • Backend: Node.js, Express
  • AI/ML: Python (Scikit-Learn, NumPy)
  • Database: PostgreSQL (Drizzle ORM)

Local Development

  1. Install dependencies:
    npm install
  2. Start the development server:
    npm run dev
  3. Open http://localhost:5000

ML Models

The Python scripts in server/ml/ train simple models on-the-fly for demonstration. For production, replace the mock training data with real datasets and save the models using joblib or pickle.

Deployment

  • Frontend: Can be deployed to Vercel (see vercel.json).
  • Backend: Can be deployed to Render or Heroku.
View this README on GitHub

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Install

npx skillfish add aryanb10/smart_agrihub