API

Disease Risk Assessment🔗

📖 Overview🔗

The disease risk assessment analytic calculates daily disease risk forecasts for corn and soybean crops based on weather conditions and validated epidemiological models. This processor leverages research from the University of Wisconsin-Madison Crop Risk Tool, implementing disease models that evaluate environmental conditions favorable to pathogen development. Using weather data, the analytic produces normalized risk scores (0-1 scale) for multiple key crop diseases, enabling proactive disease management and treatment timing decisions. The processor automatically handles geometry conversion, retrieves historical and forecast weather data, and computes moving averages and cumulative metrics required by each disease model.

🗂️ Baseline Data🔗

The analytic uses comprehensive weather data including temperature, humidity, dew point, wind speed, and precipitation EarthDaily weather service. Both historical and forecast weather data are retrieved for a specified date range and processed through validated disease models specific to each pathogen. For computational consistency, polygon geometries are automatically converted to their centroid for weather data sampling within the defined AOI (1).

1. AOI (Area of Interest)🔗

   User-defined area for analysis. Usually defined as WKT.

⚙️ API🔗

🦠 Diseases Covered🔗

Corn Diseases🔗

• Tar Spot - Phyllachora maydis
• Gray Leaf Spot - Cercospora zeae-maydis
• Gibberella Ear Rot - Fusarium graminearum

Soybean Diseases🔗

• White Mold (Dryland) - Sclerotinia sclerotiorum
• White Mold (Irrigated - 15" row spacing) - Sclerotinia sclerotiorum
• White Mold (Irrigated - 30" row spacing) - Sclerotinia sclerotiorum
• Frogeye Leaf Spot - Cercospora sojina

All risk values are normalized to a 0–1 scale, where 1 = highest modeled risk.

⚙️ Parameters & Variables🔗

Input Parameters🔗

Request Body🔗

Accepted Geometry Types: - POINT: Used directly for weather data lookup - POLYGON: Automatically converted to centroid for weather sampling - SeasonField ID: External ID from EarthDaily

Output Variables🔗

📋 Example Response🔗

{
  "id": "",
  "data": {
    "geometry": "POINT(-104.50231 40.37723)",
    "risks": [
      {
        "date": "2025-12-01",
        "tar_spot": 0,
        "gray_leaf_spot": 0.0003109,
        "gibberella_ear_rot": 1.86e-11,
        "white_mold_dry": 0,
        "white_mold_irr_15": 0.0000009677,
        "white_mold_irr_30": 0.0000000895,
        "frogeye_leaf_spot": 0.05306
      },
      {
        "date": "2025-12-02",
        "tar_spot": 0,
        "gray_leaf_spot": 0.0002839,
        "gibberella_ear_rot": 2.50e-12,
        "white_mold_dry": 0,
        "white_mold_irr_15": 0.000000630,
        "white_mold_irr_30": 0.0000000583,
        "frogeye_leaf_spot": 0.05187
      }
    ]
  }
}

⚠️ Error Management🔗

🌦️ Weather Data Requirements🔗

The processor automatically retrieves and processes comprehensive weather data:

Daily Weather Variables🔗

• Temperature (mean, max, min)
• Dew point (min)
• Relative humidity (max, mean)
• Wind speed (max at 10m)
• Precipitation (cumulative)

Hourly Weather Variables🔗

• Relative humidity (for calculating hours above thresholds)

Computed Metrics🔗

The processor calculates various moving averages and cumulative metrics required by disease models:

• Moving Averages: 14-day, 21-day, and 30-day periods for temperature, humidity, dew point, and wind speed
• Cumulative Metrics: Days with temperature >25°C, days with precipitation, hours with RH >80%, hours with RH >90% (nighttime)
• Shifted Values: 7-day lagged weather data for temporal disease model inputs

Extended Date Range🔗

Weather data is retrieved with extended boundaries to enable moving average calculations: - Extended start: startDate - 30 days - Extended end: endDate + 9 days

📊 Performance and Accuracy🔗

• Processing Mode: Synchronous - results returned immediately with POST response

• Data Source: EarthDaily Weather API (historical and forecast weather data)

• Scientific Foundation: Based on University of Wisconsin-Madison Crop Risk Tool validated disease models

• Supported Crops:

  • Corn
  • Soybeans

• Geometry Handling:

  • Point geometries: Used directly
  • Polygon geometries: Automatically converted to centroid
  • Ensures stable performance for large field polygons

💼 Use Case and Product Integration🔗

This analytic is used in:

• **Territory Insights

Key Applications:🔗

• Proactive disease management - Early warning for disease-favorable conditions
• Treatment timing optimization - Data-driven fungicide application decisions
• Risk assessment - Comparative disease pressure across fields and regions
• Forecasting - Future disease risk based on weather forecasts

⚠️ Important Notes🔗

Geometry Processing🔗

• All polygon geometries are automatically reduced to their centroid for weather data lookup
• This ensures computational consistency with the original Wisconsin-Madison disease models
• Provides stable performance regardless of field polygon size or complexity

Weather Data Integration🔗

• Both historical observations and forecast data are incorporated

Risk Score Interpretation🔗

• All risk values are normalized to a 0-1 scale
• 0 = No modeled disease risk under current conditions
• 1 = Highest modeled disease risk based on optimal pathogen conditions
• Risk scores represent relative disease favorability, not absolute infection probability

Disease Model Specificity🔗

• Each disease model uses different combinations of weather variables
• Models account for crop-specific factors (e.g., row spacing for white mold)
• Temperature, humidity, and moisture conditions are primary risk drivers
• Some models incorporate lagged weather effects (e.g., 7-day historical conditions)

Operational Advantages🔗

• Fully integrated API - No weather extraction required
• Scalable architecture - Bulk processing capability through serverless infrastructure