The Crop Prediction System is an advanced machine learning project designed to help farmers and agricultural professionals make informed decisions about which crops to plant. By analyzing various environmental and soil parameters, the system can predict the most suitable crop for a given set of conditions, thereby optimizing yield and sustainability.

https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset

• Input Parameters:

  • Nitrogen (N) content in the soil
  • Phosphorus (P) content in the soil
  • Potassium (K) content in the soil
  • Temperature (°C)
  • Humidity (%)
  • Soil pH level
  • Rainfall (mm)

• Machine Learning Model:

  • Utilizes a Random Forest Classifier trained on a dataset of crop conditions and yields.

• Model Accuracy:

  • The system has been evaluated for accuracy and reliability, achieving an accuracy rate of 99.54%.

• Frontend:

  • Built using HTML, CSS, and JavaScript.

• Backend:

  • Machine learning model developed using Python.

• Deployment:

  • Flask is used to develop Web App along with HTML, CSS and JS.