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Satellite-powered crop intelligence at scale
Farmers in developing regions lose 30-40% of potential yield to undetected crop diseases, pest infestations, and suboptimal irrigation timing. Traditional agricultural monitoring is manual, slow, expertise-dependent, and inaccessible to smallholder farmers who produce 80% of food in developing countries.
Agricultural AI is a $4B market growing at 25% CAGR. Two converging trends: (1) satellite imagery resolution reached 3m/pixel at accessible costs, and (2) edge ML inference became viable on mid-range smartphones. The gap: no solution connected satellite intelligence to actionable, localized advisory.
ML pipeline: Sentinel-2 satellite ingestion → atmospheric correction → multi-spectral band analysis → disease classification → localized advisory generation
Temporal analysis engine comparing NDVI, EVI, and moisture indices across growth cycles to detect anomalies before visible symptoms appear
Offline-first React Native architecture with SQLite local cache, background sync queue, and delta-based data transfer for low-bandwidth environments
Weather data integration for predictive risk modeling — combining humidity, temperature, and crop stage for disease probability scoring
Serverless inference pipeline on AWS Lambda with model artifacts in S3 and CloudFront for global edge distribution
Multi-language advisory system generating actionable recommendations in 8 languages with region-specific agricultural practices
Sentinel-2 data arrives as Level-1C products. Pipeline applies atmospheric correction, clips to field boundaries using PostGIS, computes vegetation indices (NDVI, EVI, NDMI), and feeds tiles into classification model. Processing: 12s per field on Lambda.
EfficientNet-B4 backbone fine-tuned on 50K labeled satellite-crop image pairs. Multi-label classification across 15 disease categories and 4 severity levels. Model distillation for on-device inference at 30fps.
SQLite with WriteAheadLogging for concurrent read/write. Background sync queue with exponential backoff and conflict resolution. Delta compression reduces sync payload by 85%.
Serverless inference with cold-start optimization (provisioned concurrency for peak hours). Satellite tiles processed in parallel. PostGIS spatial indexing enables sub-100ms field lookups across millions of polygons. Offline-first mobile architecture scales without proportional backend load.
Freemium per-hectare monitoring: free tier covers up to 5 hectares with weekly scans. Pro tier ($2-5/hectare/season) adds daily monitoring, predictive alerts, and historical analysis. Enterprise API for agricultural cooperatives and insurance companies.
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