🌱 Crop Recommendation System | Machine Learning Project | React + Flask | Hackathon Project
🌱 Crop Recommendation System | Machine Learning Project | React + Flask
In this video, I showcase my Crop Recommendation System, a full-stack Machine Learning web application that helps farmers and agriculture enthusiasts select the most suitable crop based on soil nutrients and environmental conditions.
The system uses parameters such as Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, soil pH, and rainfall to predict the best crop to grow using a trained machine learning model.
This project demonstrates the complete ML workflow — from model training to backend API development and a modern React-based frontend.
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🚀 Key Features
• 🌾 Machine Learning based crop prediction
• ⚛️ React frontend with interactive UI
• 🔗 Flask REST API for model integration
• 📊 Real-time crop recommendation
• 🌍 Deployed full-stack ML application
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🧠 Technologies Used
• Python
• Machine Learning (Scikit-Learn)
• Flask (Backend API)
• React.js (Frontend)
• HTML, CSS, JavaScript
• Render (Deployment)
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🌐 Live Project Demo
👉 Visit the website:
https://crop-frontend-6hx9.onrender.com/
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💻 GitHub Repository
👉 Source Code:
https://github.com/Harshgill77/stachack
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🎯 What You’ll Learn From This Project
• How to build an ML-powered recommendation system
• Integrating React frontend with Flask backend
• Deploying a real-world ML application
• Practical use of AI in agriculture
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📌 YouTube Tags / Keywords
Crop Recommendation System, Machine Learning Project, React ML Project, Flask API, AI in Agriculture, Full Stack ML Project, Python Data Science
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