A 5,000-acre farm operation was losing significant yield to late-detected disease, inconsistent irrigation, and pest damage. Field scouts couldn't cover enough ground quickly enough. We built an AI monitoring system using drone and satellite imagery that provides field-level crop health assessments, early disease detection, irrigation optimization, and yield prediction.
Agricultural imagery analysis requires understanding subtle color variations indicating stress before visible symptoms appear. Different crops, soil types, and growth stages all affect what 'healthy' looks like. The system needed to work with both high-resolution drone imagery and lower-resolution satellite data for daily monitoring of large acreage.
We trained crop-specific models on multispectral imagery annotated by agronomists. The system uses satellite imagery for daily broad monitoring and triggers drone flights when anomalies are detected for detailed diagnosis. NDVI and other vegetation indices are combined with weather data and soil moisture sensors for a complete picture. The AI generates field-level prescriptions for irrigation and treatment that integrate with variable-rate application equipment.
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Request a DemoThe AI spotted a fungal outbreak 10 days before our scouts would have found it. That early warning saved an entire field.
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