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tyagisurya001/
crop-recommendation-ml

Name: crop-recommendation-ml
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🌱 Crop Recommendation Model

Overview

This project provides a machine learning model that recommends the most suitable crop based on soil nutrients and environmental conditions.

The model takes numerical inputs such as nitrogen, phosphorus, potassium levels, and weather conditions, and predicts the best crop to cultivate.

Input Features

The model expects the following inputs:

Nitrogen (N)
Phosphorus (P)
Potassium (K)
Temperature (°C)
Humidity (%)
pH value
Rainfall (mm)

Output

Predicted crop label (e.g., rice, wheat, maize, etc.)

Files in Repository

crop_model.pkl → Trained machine learning model
requirements.txt → Required dependencies
app.py → (Optional) Streamlit app for UI
README.md → Project documentation

Installation

Clone the repository and install dependencies:

pip install -r requirements.txt

Usage

Example of loading and using the model:

python

import joblib
import numpy as np

# Load model
model = joblib.load("crop_model.pkl")

# Example input
data = np.array([[90, 40, 40, 25, 80, 6.5, 200]])

# Predict
prediction = model.predict(data)

print("Recommended Crop:", prediction[0])

Requirements

Typical dependencies:

scikit-learn
numpy
joblib
(optional) streamlit

Notes

Ensure input values are in the same format as used during training
If preprocessing (scaling/encoding) was used, corresponding files must also be loaded
Model performance depends on the quality of training data

Future Improvements

Add real-time weather API integration
Improve model accuracy with more data
Deploy as a full web application

License

This project is for educational and demonstration purposes.

tyagisurya001/crop-recommendation-ml