Predict for better

Crop Recommendation System

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🌾 Crop Recommendation System

The dataset is taken from kaggle from https://www.kaggle.com/datasets/madhuraatmarambhagat/crop-recommendation-dataset

📝 Problem Statement

Farmers often struggle to select the most suitable crop for cultivation due to changing soil and environmental conditions. This project builds a machine learning model that recommends the most appropriate crop based on key parameters like soil nutrients, temperature, humidity, pH, and rainfall.

The goal is to help farmers make data-driven decisions to improve productivity and sustainability.

📊 Dataset Description

The dataset used is Crop_recommendation.csv and contains the following columns:

Features (Inputs):

  • N: Nitrogen content in soil
  • P: Phosphorus content in soil
  • K: Potassium content in soil
  • temperature: Temperature in °C
  • humidity: Relative humidity in %
  • ph: pH value of the soil
  • rainfall: Rainfall in mm

Target (Output):

  • label: The recommended crop to grow (e.g., rice, maize, chickpea, etc.)

🚀 Project Workflow

1. Data Reading

  • Loaded the dataset using Pandas.

2. Data Evaluation

  • Displayed basic statistics and checked for null or missing values.
  • Confirmed data types and feature ranges.

3. Data Visualization

  • Used box plots to visualize each feature and identify outliers.

4. Feature Importance

  • Used Random Forest to identify which features most influence crop recommendation.

5. Label Encoding

  • Converted the crop labels (strings) into numeric format using LabelEncoder.

6. Feature and Target Separation

  • Split the dataset into:

    • X → input features: [N, P, K, temperature, humidity, ph, rainfall]
    • y → target crop label

7. Outlier Removal

  • Removed extreme values based on box plot analysis to improve model robustness.

8. Apply SMOTE

  • Balanced the dataset using SMOTE to generate synthetic examples of minority crop classes.

9. Preprocessing

  • Applied standard scaling to normalize feature values, which improves model performance (especially for SVM).

10. Model Training

  • Trained the following models:

    • SVM (Support Vector Machine)
    • Random Forest Classifier

11. Cross-Validation

  • Performed 5-fold cross-validation for both models:

    • SVM Average Accuracy: 97.3%
    • Random Forest Average Accuracy: 99.4% ✅ (Best Model)

12. Model Saving

  • Saved the best-performing model (Random Forest) using pickle as crop_model.pkl.

🌱 Solution

Crop Recommendation System

An app that helps farmers or users find the best crop to grow based on soil nutrients and weather conditions. It uses a machine learning model to predict the most suitable crop by analyzing inputs like nitrogen, phosphorus, potassium levels, temperature, humidity, soil pH, and rainfall.

The app has three main pages:

  1. Home: Introduces the system and explains what features are used for crop prediction.
  2. Predict Crop: Allows users to input soil and climate data to get a recommended crop.
  3. Data Info: Shows an overview of the dataset used to train the model, including sample data, statistics, and crop distribution.

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