crop-recommendation-ml

🌱 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:

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.