---
license: mit
datasets:
  - custom
metrics:
  - mean_squared_error
  - mean_absolute_error
  - r2_score
model_name: Fertilizer Recommendation System
tags:
  - random-forest
  - regression
  - multioutput
  - classification
  - agriculture
  - soil-nutrients
---

# Fertilizer Application Recommendation System

## Overview

This model predicts the fertilizer requirements for various crops based on input features such as crop type, target yield, field size, and soil properties. It utilizes a combination of Random Forest Regressor and Random Forest Classifier to predict both numerical values (e.g., nutrient needs) and categorical values (e.g., fertilizer application instructions).

## Training Data

The model was trained on a custom dataset containing the following features:

- Crop Name
- Target Yield
- Field Size
- pH (water)
- Organic Carbon
- Total Nitrogen
- Phosphorus (M3)
- Potassium (exch.)
- Soil moisture

The target variables include:

**Numerical Targets**:
- Nitrogen (N) Need
- Phosphorus (P2O5) Need
- Potassium (K2O) Need
- Organic Matter Need
- Lime Need
- Lime Application - Requirement
- Organic Matter Application - Requirement
- 1st Application - Requirement (1)
- 1st Application - Requirement (2)
- 2nd Application - Requirement (1)

**Categorical Targets**:
- Lime Application - Instruction
- Lime Application
- Organic Matter Application - Instruction
- Organic Matter Application
- 1st Application
- 1st Application - Type fertilizer (1)
- 1st Application - Type fertilizer (2)
- 2nd Application
- 2nd Application - Type fertilizer (1)

## Model Training

The model was trained using the following steps:

1. **Data Preprocessing**:
   - Handling missing values
   - Scaling numerical features using `StandardScaler`
   - One-hot encoding categorical features

2. **Modeling**:
   - Splitting the dataset into training and testing sets
   - Training a `RandomForestRegressor` for numerical targets using a `MultiOutputRegressor`
   - Training a `RandomForestClassifier` for categorical targets using a `MultiOutputClassifier`

3. **Evaluation**:
   - Evaluating the models using the test set with metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) Score for regression, and accuracy for classification.

## Evaluation Metrics

The model was evaluated using the following metrics:

- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- R-squared (R2) Score
- Accuracy for categorical targets

## How to Use

### Input Format

The model expects input data in JSON format with the following fields:

- "Crop Name": String
- "Target Yield": Numeric
- "Field Size": Numeric
- "pH (water)": Numeric
- "Organic Carbon": Numeric
- "Total Nitrogen": Numeric
- "Phosphorus (M3)": Numeric
- "Potassium (exch.)": Numeric
- "Soil moisture": Numeric

### Preprocessing Steps

This script includes:

    Loading the models and preprocessor.
    Defining the categorical and numerical targets.
    Loading the label encoders.
    Creating a function make_predictions that processes the input data, makes predictions, and decodes the categorical predictions.

### Inference Procedure

```python
import pandas as pd
from joblib import load
from huggingface_hub import hf_hub_download
from sklearn.preprocessing import LabelEncoder

# Load models and preprocessor
preprocessor_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='preprocessor.joblib')
numerical_model_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='numerical_model.joblib')
categorical_model_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='categorical_model.joblib')

preprocessor = load(preprocessor_path)
numerical_model = load(numerical_model_path)
categorical_model = load(categorical_model_path)

# Define categorical targets
categorical_targets = [
    'Lime Application - Instruction',
    'Lime Application',
    'Organic Matter Application - Instruction',
    'Organic Matter Application',
    '1st Application',
    '1st Application - Type fertilizer (1)',
    '1st Application - Type fertilizer (2)',
    '2nd Application',
    '2nd Application - Type fertilizer (1)',
    '1st Application_1',
    '1st Application - Type fertilizer (1)_3',
    '1st Application - Type fertilizer (2)_5',
    '2nd Application_6',
    '1st Application_21',
    '1st Application - Type fertilizer (1)_23',
    '1st Application - Type fertilizer (2)_25',
    '2nd Application_26',
    '2nd Application - Type fertilizer (1)_28'
]

# Define numerical targets
numerical_targets = [
    'Nitrogen (N) Need',
    'Phosphorus (P2O5) Need',
    'Potassium (K2O) Need',
    'Organic Matter Need',
    'Lime Need',
    'Lime Application - Requirement',
    'Organic Matter Application - Requirement',
    '1st Application - Requirement (1)',
    '1st Application - Requirement (2)',
    '2nd Application - Requirement (1)'
]

# Load label encoders
label_encoders = {col: load(hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename=f'label_encoder_{col}.joblib')) for col in categorical_targets}

def make_predictions(input_data):
    # Convert input data to DataFrame
    input_df = pd.DataFrame([input_data])

    # Preprocess the input data
    X_transformed = preprocessor.transform(input_df)

    # Predict with numerical model
    numerical_predictions = numerical_model.predict(X_transformed)

    # Predict with categorical model
    categorical_predictions_encoded = categorical_model.predict(X_transformed)

    # Decode categorical predictions
    categorical_predictions_decoded = {}
    for i, col in enumerate(categorical_targets):
        le = label_encoders[col]
        try:
            categorical_predictions_decoded[col] = le.inverse_transform(categorical_predictions_encoded[:, i])
        except ValueError as e:
            categorical_predictions_decoded[col] = ["Unknown"] * len(categorical_predictions_encoded[:, i])

    # Combine numerical and categorical predictions into a dictionary
    predictions_combined = {col: numerical_predictions[0, i] for i, col in enumerate(numerical_targets)}
    predictions_combined.update({col: categorical_predictions_decoded[col][0] for col in categorical_targets})

    return predictions_combined

# Example usage
input_data = {
    'Crop Name': 'maize(corn)',
    'Target Yield': 3600.0,
    'Field Size': 1.0,
    'pH (water)': 6.1,
    'Organic Carbon': 11.4,
    'Total Nitrogen': 1.1,
    'Phosphorus (M3)': 1.8,
    'Potassium (exch.)': 3.0,
    'Soil moisture': 20.0
}

predictions = make_predictions(input_data)

print("Predicted Fertilizer Requirements:")
for col, pred_value in predictions.items():
    print(f"{col}: {pred_value}")