THE CHALLENGE
Why Traditional Monitoring Falls Short
Early intervention is key to minimizing crop loss, but traditional methods cannot deliver it at scale.
Late Detection
Visible symptoms appear 2-3 weeks after stress begins. By then, yield loss has already occurred
Manual Scouting
Field scouting is slow, subjective, labor-intensive, and impossible at scale
Fragmented Coverage
Large or scattered landholdings cannot be monitored consistently or comprehensively
Wasteful Inputs
Blanket application of fertilizer, water, and pesticides wastes resources and reduces margins
Up to 90%
Field-level precision
2-3 Weeks
Early Stress Detection
15+
Data Formats Supported
100%
Explainable AI Results
One Unified Workspace for Crop Health Analysis
Satellite imagery
Weather data
Soil and field boundaries
Historical crop performance
All aligned in a shared environment to analyze crop health and stress patterns over time
GeoVista enables:
- Integrated analysis at field and sub-field scale
- AI workflows applied across time-series imagery and indices
- Clear, visual outputs that support agronomic interpretation
Automate, Analyze, Alert
GeoVista AI automates acquisition and analysis of imagery, classifies stress at field and sub-field scale, and delivers custom alerts pointing to emerging issues days or weeks ahead of visible symptoms.
Observation
Weekly imagery from satellites and on-demand drone flights covering your fields
Processing
NDVI and similar indices computed with atmospheric correction for accurate vigor and stress mapping
Modeling
ML models map stress zones based on yield history, weather, and field context with trend flagging
Interpretation
Zone maps generated for targeted field visits, input optimization, or yield prediction
Why GeoVista AI
Scalability
Monitor thousands of fields with different crop types simultaneously
Precision
Drill down to the resolution of available imagery
Proactive
Alerts for input managers to act before damage spreads
Use cases
From field-level monitoring to regional assessment
Crop Health Monitoring
Continuous visibility into crop conditions across fields and seasons
Early Stress Detection
Identify emerging stress before visible symptoms appear
Input Optimization
Target water, fertilizer, and treatments where they are most needed
Yield Variability Analysis
Understand spatial and temporal differences in crop performance
Multi-Farm Monitoring
Track crop health consistently across large and distributed operations
Regional Crop Assessment
Analyze crop conditions at the farm, district, or regional scale
Clear, Actionable Outputs
Everything you need to move from monitoring to action
- Version-controlled models
- Full audit trails
- Access-governed outputs
- Field validation support
Temporal Comparisons
Season-over-season crop health analysis
Priority Action Areas
Zones ranked for targeted field activity
Defensible Reports
Data-backed insights for agronomic and operational decisions
Wimmera, South Victoria — Yield Impact Analysis
Correlating vegetation indices with precipitation data to explain year-over-year yield variation
Vegetation Index Analysis
NDVI imagery across three phenological phases — Sowing (April), Growth (July), and Harvesting (August) — for 2023 and 2024. Although similar amounts of crops were planted during sowing, the growth and harvesting phases in 2023 show significantly higher vegetation activity and crop yield compared to 2024.
Precipitation Correlation
Hourly accumulated precipitation from ERA5 data reveals the cause: during the critical growth phase, 2023 received 0.004m of rainfall compared to just 0.0015m in 2024. This water deficit directly correlates with the observed crop stress and reduced yield in 2024 — insights only visible through integrated spatial-temporal analysis.
“GeoVista AI's integrated analysis identified the rainfall deficit as the primary yield limiter — enabling proactive irrigation planning for future seasons.”
How GeoVista is Different
Built for large-scale crop monitoring
Interpretable Stress Signals
Stress insights agronomists can review and trust
Cross-Source Field Context
Imagery, weather, and field data analyzed together
Season-Over-Season Tracking
Track persistent issues across multiple seasons
Sub-Field Resolution
Granular visibility into variability within individual fields.
Operational Scale
Consistent monitoring across thousands of fields and crop types.
Production-Ready Platform
Access-controlled outputs designed for enterprise agricultural workflows.
Agriculture Professionals
GeoVista AI adapts across crop types, regions, and climatic zones
Large Producers
Multi-farm operations seeking scalable monitoring
Input Suppliers
Enable precision input advice for clients
Agri Consultants
Enhance advisory with spatial intelligence
Cooperatives
Coordinate across member farms
Government Agencies
Regional crop and food security assessment
Insurance
Data-driven underwriting and claims
Choose Your Deployment Option
On-premises Deployment
Total control. No data leaves your network
Cloud Deployment
Geographic distribution for global operations
Built on Trust. Backed by Global Standards.
Certified for top-tier information security management.
Certified for rigorous data security and operational integrity across systems.
Process maturity certified for dependable delivery.
Global standard for consistent quality and reliability.
Find What Matters, Faster
GeoVista AI helps teams monitor crop health, detect stress sooner, and support data-backed decisions across agricultural operations.
Frequently Asked Questions
This product is co-engineer & powered by Kalpa and HestaBit.
GeoVista detects crop health by processing satellite imagery and generating NDVI layers that visually represent vegetation condition across agricultural areas.
Crop health detection in GeoVista requires a defined area of interest and satellite datasets for the selected time period. No field sensors or manual inputs are required.
Yes. GeoVista allows users to compare NDVI layers from different dates or seasons to evaluate changes in crop-related vegetation over time.
No. GeoVista does not assign health labels or scores. It provides visual NDVI outputs that users interpret based on vegetation intensity and spatial patterns.
Yes. GeoVista supports multi-site analysis, enabling users to run the same crop health detection workflow across multiple agricultural areas.
Yes. GeoVista is designed to scale from individual fields to regional agricultural monitoring, using consistent satellite-based workflows.
See what your data can show you with GeoVista
Load your datasets, run your first workflow, and get prospectivity results you can review today.