Agriculture Template

Four-agent precision farming pipeline — multispectral imagery analysis, pest and disease detection, targeted spray recommendations, and yield forecasting.

Analyse drone or satellite imagery, detect crop stress and pests before they spread, and receive actionable treatment plans — all from a single workflow call.

Analyse drone or satellite imagery, detect crop stress and pests before they spread, and receive actionable treatment plans — all from a single workflow call.

from examples.cookbooks.Industry_Templates.agriculture_template import precision_agriculture_workflow

result = precision_agriculture_workflow("Field 12 multispectral scan — NDVI drop in north quadrant")
print(result)

The user uploads crop imagery; agents detect stress, pests, and irrigation recommendations per field.

Quick Start

1. Run the prebuilt workflow for multiple fields

   from praisonaiagents import Agent, tool
   from examples.cookbooks.Industry_Templates.agriculture_template import precision_agriculture_workflow
   
   fields  = ["FIELD-001", "FIELD-002", "FIELD-003"]
   weather = {"temperature": 25, "humidity": 60, "wind_speed": 8, "precipitation": 0}
   
   result = precision_agriculture_workflow(fields, "wheat", weather)
   
   print(result["alerts"])
   print(result["yield_forecasts"])
   print(f"Sustainability score: {result['sustainability_score']:.0f}/100")
   
2. Use IoT sensor network agent for real-time data

   from examples.cookbooks.Industry_Templates.agriculture_template import IoTSensorPatterns
   
   sensor_agent = IoTSensorPatterns.create_sensor_network_agent(
       sensor_types=["soil_moisture", "temperature", "humidity", "ph"]
   )
   
   status = sensor_agent.start("Collect sensor readings from all FIELD-001 nodes")
   print(status)
   

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How It Works

Agent	Responsibility	SLA
MultispectralAnalyzer	Calculate NDVI/NDRE/CCCI vegetation indices and identify stress zones	≤ 2 min per field
DiseaseIdentifier	AI-based pest and disease classification with severity level	≤ 30 s
SprayRecommender	Targeted spray strategy — product, rate, GPS zones, weather window	≤ 1 min
YieldPredictor	Harvest yield forecast with quality grade and market value estimate	≤ 45 s

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Configuration Options

Pydantic I/O schemas used by this template:

Schema	Key Fields
MultispectralData	field_id, ndvi_index, ndre_index, ccci_index, moisture_level, temperature, affected_area, gps_coordinates
PestDiseaseReport	report_id, field_id, pest_type, disease_type, severity, affected_crops, spread_rate, confidence_score
SprayRecommendation	recommendation_id, field_id, treatment_type, product_name, application_rate, target_zones, optimal_time, cost_estimate, environmental_impact
YieldForecast	forecast_id, field_id, crop_type, predicted_yield, confidence_interval, harvest_window, quality_grade, market_price_estimate

Severity levels (used in PestDiseaseReport)

Level	Meaning	Typical action
none	No threat detected	Routine fertiliser application
low	Early blight signs	Organic treatment (e.g. Neem Oil)
moderate	Localised infestation	Targeted fungicide/pesticide
high	Spreading infestation	Systemic treatment + monitoring
severe	Field-wide outbreak	Emergency intervention

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Common Patterns

Integrated Pest Management (IPM) strategy by severity

from examples.cookbooks.Industry_Templates.agriculture_template import SustainableFarmingPatterns

strategy = SustainableFarmingPatterns.integrated_pest_management("moderate")
print(strategy)

Precision irrigation based on soil moisture

from examples.cookbooks.Industry_Templates.agriculture_template import SustainableFarmingPatterns

irrigation = SustainableFarmingPatterns.precision_irrigation(
    moisture_level=25.0,
    crop_stage="flowering"
)
print(f"Irrigate: {irrigation['irrigation_needed']}, Amount: {irrigation['amount_mm']} mm")

Recommend next crop for rotation

from examples.cookbooks.Industry_Templates.agriculture_template import SustainableFarmingPatterns

next_crop = SustainableFarmingPatterns.crop_rotation_optimizer("wheat", soil_data={})
print(f"Next crop: {next_crop}")

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Best Practices

Fly imagery in optimal lighting conditions

MultispectralAnalyzer accuracy degrades significantly in overcast conditions or when shadows cover more than 20 % of a field. Schedule drone flights within 2 hours of solar noon for best NDVI readings.

Act on high-severity alerts within 24 hours

DiseaseIdentifier reports spread_rate in % per day. At high severity (spread_rate > 2 %), a one-day delay can double the affected area. The workflow automatically generates a SprayRecommendation for high/severe findings.

Monitor the sustainability_score trend

The workflow calculates a sustainability_score (0–100) based on total chemical application. Track this per season — a declining score signals overuse of pesticides, which correlates with long-term soil health degradation.

Validate GPS spray zones before field application

SprayRecommendation.target_zones contains GPS polygon boundaries. Always cross-check these against your field boundary GIS layer to prevent off-target application near water bodies or buffer zones.

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