docs/CLI_USAGE.md

CLI Usage Guide - Crop Yield Predictor

🌾 Overview

The crop_yield_predictor.py script provides three modes for making crop yield predictions:

1. One-shot CLI prediction - Enter parameters via command line flags
2. Batch CSV prediction - Process multiple records from a CSV file
3. Interactive mode - Step-by-step input prompts

📋 Prerequisites

Ensure you have trained models available:

python crop_yield_ml_pipeline.py

🚀 Usage Examples

1. One-Shot CLI Prediction

Make a single prediction by providing all parameters as command-line arguments:

python crop_yield_predictor.py predict \
    --year 2024 \
    --state "Punjab" \
    --crop "Rice" \
    --season "Kharif" \
    --area 10.0 \
    --production 25.0 \
    --rainfall 1200 \
    --fertilizer 75 \
    --pesticide 8

Output:

🚀 Initializing Crop Yield Predictor...
📥 Loading trained models...
✅ All models loaded successfully

Prediction results (kg/hectare):
- Random Forest: 2017.70
- XGBoost: 1875.01
- PyTorch: 1103.98
Best model: Random Forest -> Yield 2017.70 kg/ha
Total expected production: 20.18 tons

2. Batch CSV Prediction

Process multiple records from a CSV file:

python crop_yield_predictor.py batch input_data.csv --out results.csv

Input CSV Format:

Crop_Year,State,District,Crop,Season,Area,Production,Annual_Rainfall,Fertilizer,Pesticide
2024,Punjab,Ludhiana,Rice,Kharif,10.0,25.0,1200.0,75.0,8.0
2024,Haryana,Karnal,Wheat,Rabi,15.0,30.0,800.0,60.0,5.0

Output: Creates a CSV with additional columns for each model's predictions.

3. Interactive Mode

Run without any arguments for step-by-step input:

python crop_yield_predictor.py

The system will guide you through entering each parameter interactively.

📝 Required Parameters

For CLI Prediction (predict mode):

Parameter	Type	Required	Description	Example
--year	int	✅	Crop year	2024
--state	str	✅	State name	"Punjab"
--crop	str	✅	Crop type	"Rice"
--season	str	✅	Growing season	"Kharif"
--area	float	✅	Area in hectares	10.0
--production	float	✅	Production in tons	25.0
--rainfall	float	❌	Annual rainfall (mm)	1200 (default: 1000)
--fertilizer	float	❌	Fertilizer usage (kg)	75 (default: 50)
--pesticide	float	❌	Pesticide usage (kg)	8 (default: 5)
--district	str	❌	District name	"Ludhiana" (default: "Unknown")

Valid Options:

States: Any Indian state (e.g., Punjab, Haryana, Gujarat, Tamil Nadu, etc.)

Crops: Rice, Wheat, Maize, Cotton, Sugarcane, Groundnut, and 60+ others

Seasons:

• Kharif - Monsoon season (June-October)
• Rabi - Winter season (November-April)
• Summer - Summer season (April-June)
• Whole Year - Year-round cultivation
• Autumn, Winter, Total - Other seasonal categories

💡 Tips

1. Use quotes for multi-word values:

   --state "Uttar Pradesh" --crop "Arhar/Tur"
   

2. Check available options by running interactive mode first to see supported states/crops

3. Batch processing is efficient for multiple predictions:

   # Process 1000 records at once
   python crop_yield_predictor.py batch large_dataset.csv --out predictions.csv
   

4. Default values are provided for optional parameters based on typical Indian agricultural practices

📊 Understanding Results

Model Predictions

• Random Forest: Ensemble of decision trees (good baseline)
• XGBoost: Gradient boosting (often most accurate)
• PyTorch: Deep neural network (handles complex patterns)

Yield Interpretation

• > 3000 kg/ha: 🟢 Excellent yield
• 2000-3000 kg/ha: 🟡 Good yield
• 1000-2000 kg/ha: 🟠 Moderate yield
• < 1000 kg/ha: 🔴 Low yield

🔧 Command Reference

# Get general help
python crop_yield_predictor.py --help

# Get help for specific mode
python crop_yield_predictor.py predict --help
python crop_yield_predictor.py batch --help

# One-shot prediction (minimal)
python crop_yield_predictor.py predict --year 2024 --state Punjab --crop Rice --season Kharif --area 10 --production 25

# Batch prediction
python crop_yield_predictor.py batch data.csv --out results.csv

# Interactive mode (default)
python crop_yield_predictor.py

🎯 Quick Examples

Rice in Punjab (Kharif season):

python crop_yield_predictor.py predict --year 2024 --state Punjab --crop Rice --season Kharif --area 5 --production 12 --rainfall 1100

Wheat in Haryana (Rabi season):

python crop_yield_predictor.py predict --year 2024 --state Haryana --crop Wheat --season Rabi --area 8 --production 18 --rainfall 600

Cotton in Gujarat (Kharif season):

python crop_yield_predictor.py predict --year 2024 --state Gujarat --crop Cotton --season Kharif --area 12 --production 8 --rainfall 800 --pesticide 15

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📁 Files:

• crop_yield_predictor.py - Main CLI predictor
• trained_models/ - Directory with trained models
• sample_batch.csv - Example input file for batch prediction

🔗 Related:

• crop_yield_ml_pipeline.py - Train the models
• test_models.py - Test model performance
• results_summary.py - View training results