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Chapter 9: Turf Disease Detection & Prevention

Learning Objectives

After completing this chapter, you will be able to: - Identify the spectral signatures of common turf diseases including dollar spot, brown patch, and pythium - Distinguish between disease-related stress and environmental stress using multi-index analysis - Implement early warning indicator systems that trigger investigation before visible symptoms emerge - Evaluate emerging treatment technologies including UVC robotic systems like GreenGuard

Key Concepts

  1. Dollar spot (Clarireedia jacksonii) detection
  2. Brown patch (Rhizoctonia solani) spectral patterns
  3. Pythium blight identification
  4. Fairy ring mapping and monitoring
  5. Fungal disease lifecycle and conditions
  6. Spectral indicators of pathogen stress vs. abiotic stress
  7. NDRE sensitivity to early chlorophyll degradation
  8. Disease pressure modeling (temperature + moisture + history)
  9. Early warning indicator frameworks
  10. UVC treatment technology (GreenGuard robots)
  11. Fungicide application timing optimization
  12. Integrated pest management (IPM) integration
  13. Historical disease mapping and recurrence prediction
  14. Ground-truthing disease identification
  15. Superintendent alert and response workflows

Summary

Turf diseases cost the golf industry hundreds of millions of dollars annually in treatment chemicals, lost playing surface, and recovery time. The traditional detection model — a superintendent or crew member spotting visual symptoms during morning inspection — means that by the time disease is identified, the pathogen has already established itself and caused irreversible damage to the affected area. Multispectral analytics shifts the detection window earlier, identifying the physiological stress that precedes visible symptoms by days to weeks.

Dollar spot, the most prevalent disease on golf course putting greens, creates small, discrete lesions that produce detectable NDRE anomalies before the characteristic tan spots become visible. Brown patch manifests as circular patterns of chlorophyll degradation that appear in NDRE imagery as expanding rings of reduced reflectance. Pythium blight, which can devastate greens overnight under the right conditions, shows subtle moisture-stress signatures in pre-dawn thermal data combined with declining NDVI in high-risk zones. Each disease creates a characteristic spatial and spectral pattern that, once learned, enables pattern-matching detection across temporal datasets.

Beyond detection, emerging technologies are changing the treatment landscape. GreenGuard and similar UVC robotic systems apply targeted ultraviolet light to kill surface pathogens without chemical inputs — a technology that pairs naturally with precision mapping. When disease-risk zones are identified through aerial analytics, UVC treatment can be directed precisely where needed rather than applied broadly. This integration of detection and targeted treatment represents the next evolution of disease management: from calendar-based preventive spraying to data-driven precision response.

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