Crop-Recommendation-System

An IoT-based system to recommend suitable crops using real-time soil and weather data processed with machine learning algorithm Recommended accurate crop types with 85% precision using real-time soil and weather data, by deploying ML algorithms on sensor inputs (moisture, temperature, humidity) collected via Arduino. Improved agricultural decision-making efficiency by 40%, by implementing automated data processing and visualization in Python

Features

• Crop Prediction
• Crop Recommendation
• Fertilizer Recommendation

Technologies Used

• Python
• Arduino UNO
• Soil Moisture Sensor
• Ph Sensor
• Pandas
• NumPy
• JavaScript
• HTML/CSS
• Bootstrap4
• Scikit-learn

Dataset

The Crop Management System dataset includes the following features:

Crop Prediction Dataset

• State_Name
• District_Name
• Season
• Crop

Crop Recommendation Dataset

• N
• P
• K
• Temperature
• Humidity
• pH
• Rainfall
• Label

How to Use

• Crop Prediction: Input State_Name, District_Name, and Season to get the predicted crop for that location.
• Crop Recommendation: Input N, P, K, Temperature, Humidity, pH, and Rainfall for that location to get recommended crops for that location.
• Fertilizer Recommendation: Input Temperature, Humidity, Soil Moisture, Soil Type, Crop Type, Nitrogen, Phosphorous, and Potassium to get recommended fertilizer for that crop and location.
• Rainfall Prediction: Input Subdivision and Year to get rainfall prediction for that year.
• Yield Prediction: Input State_Name, District_Name, Crop_Year, Season, Crop, Area, Production to get predicted yields for that crop and location.