Early Stress Map

Detect crop stress up to ten days before NDVI decline using thermal and multispectral analytics, enabling proactive interventions for yield protection.

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How does it work?

Traditional NDVI monitoring detects crop stress only after visible canopy changes, delaying interventions and increasing yield loss risk. Precursor mapping leverages thermal anomalies and early spectral shifts to identify stress signals up to ten days sooner, enabling timely management actions.

Early Detection: Identifies thermal or spectral stress indicators up to ten days before NDVI decline. This lead time allows agronomists to respond before visible symptoms appear.
Proactive Management: Provides actionable insights for targeted interventions such as irrigation or fertilization. This reduces crop damage and optimizes input use.
Yield Optimization: By addressing stress early, farmers can maintain optimal growth conditions and avoid yield penalties. Studies show potential yield increases of 5–10% with early interventions.
Water Stress Profiling: Maps crop water stress through thermal imaging, pinpointing areas requiring irrigation. This supports precise water allocation and conserves resources.
Disease & Pest Alerts: Detects subtle stress patterns associated with disease or pest pressure before outbreaks. Early warnings facilitate targeted scouting and treatment, minimizing spread.
Scalable Integration: Processes satellite and drone data across thousands of hectares for regional monitoring. Seamless API integration connects to farm management systems for automated workflows.

Frequently asked questions

Have a different question and can’t find the answer you’re looking for? Reach out to our support team by sending us an email and we’ll get back to you as soon as we can.

How does precursor mapping compare to standard NDVI monitoring?: Precursor mapping combines thermal and spectral data to detect stress before canopy reflectance changes trigger NDVI drops. Standard NDVI waits for visible decline, resulting in delayed alerts and responses.
Why is early stress mapping gaining popularity among agronomists?: Agronomists appreciate the extended lead time for interventions, which reduces crop loss and input waste. Proven yield improvements and resource savings drive its adoption across farms and research trials.
What are the limitations of early stress mapping?: Data accuracy depends on sensor resolution, atmospheric conditions, and calibration, which can introduce false positives. Frequent cloud cover may limit thermal and multispectral acquisitions, reducing update frequency.
Can this solution integrate with existing farm management platforms and irrigation systems?: Yes, standard APIs allow seamless integration with common farm management and variable-rate irrigation tools. Alerts can trigger automated irrigation adjustments or management tasks through platform workflows.
Are there alternatives to precursor mapping for early stress detection?: Other indices like PRI or chlorophyll fluorescence can indicate stress but often require specialized sensors or laboratory calibration. Precursor mapping leverages widely available thermal and multispectral imagery for broader accessibility.
What data sources and update frequency are supported?: The platform supports high-resolution drone imagery and satellite data from sensors like Landsat, Sentinel, or commercial constellations. Typical update cycles range from daily drone flights to weekly satellite passes, based on source availability.

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