crops-predictor

🌱 Crop Prediction Web App

A full-stack Crop Prediction web application that helps farmers and agricultural users determine the best crop to grow based on real-time environmental and soil data. The project includes a machine learning-powered backend and a modern ReactJS frontend.

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🗂 Project Structure

siddharthdrona-crops-predictor/
├── README.md                   # Root project documentation
├── backend/                   # Flask backend API and ML model
│   ├── app.py
│   ├── crop\_data.csv
│   ├── crop\_model.pkl
│   ├── feature\_selector.pkl
│   └── model\_train.py
└── crop-predictor/            # ReactJS frontend
├── README.md
├── package.json
├── public/
│   ├── index.html
│   ├── manifest.json
│   └── robots.txt
└── src/
├── App.css
├── App.js
├── App.test.js
├── index.css
├── index.js
├── reportWebVitals.js
├── setupTests.js
└── pages/
├── CropDetailPage.js
├── Login.js
├── Predictform.css
├── PredictForm.js
├── Register.js
└── ResultPage.js

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🌾 Features

• Predicts most suitable crop based on:
  • Temperature
  • Humidity
  • pH
  • Soil type
  • Land size (acres)
  • Water availability
• ML model built using Random Forest
• User-friendly ReactJS frontend
• LocalStorage-based login/register system
• Image and seasonal information for each crop
• Multilingual support (e.g., Hindi descriptions)
• Extendable for chatbot & MSP support (future scope)

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⚙️ Technologies Used

• Frontend: ReactJS, CSS, JSX
• Backend: Python, Flask, Scikit-learn
• Model: Random Forest Classifier
• Other: Axios, LocalStorage

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/<your-username>/siddharthdrona-crops-predictor.git
cd siddharthdrona-crops-predictor

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2. Backend Setup

cd backend
pip install -r requirements.txt  # manually list or create this file
python app.py

Backend runs at: http://localhost:5000

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3. Frontend Setup

cd crop-predictor
npm install
npm start

Frontend runs at: http://localhost:3000

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🧪 Sample API Request

POST /predict

{
  "temperature": 28,
  "humidity": 70,
  "ph": 6.5,
  "soil": "loamy",
  "acres": 2,
  "water": 2000
}

Response:

{
  "crop": "rice",
  "confidence": 0.91
}

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📌 To-Do / Future Enhancements

• 🔁 Add chatbot with OpenAI for crop-related queries
• 💬 Voice-based crop interaction
• 💸 Show Minimum Support Price (MSP) per crop
• ☁️ Integrate real-time weather API
• 🧾 Save user prediction history in database

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📜 License

MIT License — feel free to use and modify.

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👨‍💻 Author

Siddharth Drona 🌐 GitHub: @SiddharthDrona