🌱 Crop Recommendation System

A Machine Learning-based Crop Recommendation System that predicts the most suitable crop based on soil nutrients and environmental conditions.

The system uses a K-Nearest Neighbors (KNN) classification algorithm and considers factors such as nitrogen, phosphorus, potassium, soil pH, temperature, humidity, and rainfall for crop prediction.

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📌 Overview

Agricultural crop selection depends on various soil and weather conditions. This project uses machine learning to recommend suitable crops based on input parameters.

The system also integrates real-time weather data using the Open-Meteo API to obtain temperature, humidity, and rainfall information.

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✨ Features

• 🌱 Crop recommendation using Machine Learning
• 🤖 K-Nearest Neighbors (KNN) classification model
• 🌦️ Real-time weather data integration
• 🧪 Uses soil nutrient parameters:
  • Nitrogen (N)
  • Phosphorus (P)
  • Potassium (K)
  • Soil pH
• 📊 Uses environmental factors:
  • Temperature
  • Humidity
  • Rainfall

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🛠️ Technologies Used

• Python 🐍
• Pandas
• Scikit-learn
• NumPy
• Requests
• Open-Meteo API

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📂 Project Structure

Crop-Recommendation-System
│
├── crop_rec_system.py          # Crop recommendation ML program
├── Crop_recommendation.csv     # Dataset
├── requirements.txt            # Required Python libraries
├── README.md                   # Project documentation
└── .gitignore                  # Git ignored files

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📊 Dataset

The dataset contains soil and environmental parameters used for crop prediction.

Input Features:

Feature	Description
N	Nitrogen content
P	Phosphorus content
K	Potassium content
temperature	Temperature value
humidity	Humidity percentage
ph	Soil pH value
rainfall	Rainfall amount

Output:

label → Recommended crop

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⚙️ Installation

Clone the repository:

git clone https://github.com/Sathyabyte/Crop-Recommendation-System.git

Navigate into the project folder:

cd Crop-Recommendation-System

Install dependencies:

pip install -r requirements.txt

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▶️ How to Run

Run the Python program:

python crop_rec_system.py

The program will:

1. Load the crop dataset
2. Train a KNN classification model
3. Fetch weather information
4. Take soil nutrient values as input
5. Predict the recommended crop

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💡 Example Input

Enter Nitrogen value: 90
Enter Phosphorus value: 42
Enter Potassium value: 43
Enter pH value: 6.5

🌾 Output

Recommended Crop: rice

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🚀 Future Improvements

• Save trained model for faster predictions
• Add a user-friendly GUI/web interface
• Improve model comparison using multiple ML algorithms
• Add location-based crop recommendations

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👩‍💻 Author

Sathyabyte

BCA (Hons) Student | Data Science + AI/ML