---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: tabular-classification
tags:
- classification
- crop-health
---

# Model Card for Infinitode/PSPM-OPEN-ARC

Repository: https://github.com/Infinitode/OPEN-ARC/

## Model Description

OPEN-ARC-PSP is a straightforward XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was designed to potentially identify plants experiencing high stress caused by external factors.

**Architecture**:

- **XGBClassifier**: `n_estimators=100`, `learning_rate=0.1`, `max_depth=6`, `subsample=0.8`, `colsample_bytree=0.8`, `random_state=42`.
- **Framework**: XGBoost
- **Training Setup**: Trained with the default training params.

## Uses

- Identifying crops experiencing significant stress.
- Improving crop production by mitigating major stressors affecting plants.
- Performing experimental studies on plant behavior and yield outcomes influenced by stress levels.

## Limitations

- May generate implausible or inappropriate results when influenced by extreme outlier values.
- Could provide inaccurate plant stress levels; caution is advised when relying on these outputs.

## Training Data

- Dataset: Plant-Health-Data dataset from Kaggle.
- Source URL: https://www.kaggle.com/datasets/ziya07/plant-health-data
- Content: Soil characteristics, moisture levels, and various agricultural metrics, combined with the anticipated stress level of the plant.
- Size: 1200 entries of plant stress levels.
- Preprocessing: Dropped unnecessary features like the `Timestamp` and `Plant_ID`. Stress levels were manually mapped to three distinct numerical values.

## Training Procedure

- Metrics: accuracy, precision, recall, F1
- Train/Testing Split: 80% train, 20% testing.

## Evaluation Results

| Metric | Value |
| ------ | ----- |
| Testing Accuracy | 99.1% |
| Testing Weighted Average Precision | 99% |
| Testing Weighted Average Recall | 99% |
| Testing Weighted Average F1 | 99% |

## How to Use

```python
import random

def test_random_samples(model, X_test, y_test, n_samples=5):
    """
    Selects random samples from the test set, makes predictions, and compares with actual values.
    
    Parameters:
    - model: Trained XGBoost classifier.
    - X_test: Feature set for testing.
    - y_test: True labels for testing.
    - n_samples: Number of random samples to test.
    
    Returns:
    None
    """
    # Convert X_test and y_test to DataFrame for easier indexing
    X_test_df = X_test.reset_index(drop=True)
    y_test_df = y_test.reset_index(drop=True)

    # Pick random indices
    random_indices = random.sample(range(len(X_test)), n_samples)
    
    print("Testing on Random Samples:")
    for idx in random_indices:
        sample = X_test_df.iloc[idx]
        true_label = y_test_df.iloc[idx]
        
        # Predict using the model
        prediction = model.predict(sample.values.reshape(1, -1))

        # Reverse the health mapping
        reverse_health_mapping = {v: k for k, v in health_mapping.items()}

        # Map true and predicted labels
        true_label_description = reverse_health_mapping[true_label]
        predicted_label_description = reverse_health_mapping[prediction[0]]
        
        # Output results
        print(f"Sample Index: {idx}")
        print(f"Features: {sample.values}")
        print(f"True Label: {true_label}, Predicted Label: {prediction[0]}")
        print(f"True Label (Description): {true_label_description}, Predicted Label (Description): {predicted_label_description}")
        print("-" * 40)

# Example usage
test_random_samples(xgb, X_test, y_test)
```

## Contact

For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact.