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Core Architecture

The IPS CeresAI Computational Core

By combining high-resolution satellite remote-sensing bands with on-site IoT telemetry models, our engineering team transforms noisy outdoor parameters into organized spatial datasets.

Interactive Blueprint

End-to-End System Architecture

Telemetry flows dynamically from multi-layered physical sensors into our cloud container nodes, triggering AI model runs and visual analytics outputs.

Satellite Feeds Drone Orthomosaics IoT Soil Probes Ingestion Gateway REST API / gRPC Computer Vision Leaf Blight Seg Geospatial AI NDVI Heatmaps SaaS Portals Client API & Web
AI & ML model telemetry data dashboard

Artificial Intelligence & ML

We train specialized deep learning models on multi-spectral imagery datasets. Our models isolate crop anomalies from surrounding soil noise and shadows with extreme precision.

Crop leaves pathogology detection analysis

Computer Vision

Using semantic segmentation architectures (built on U-Net and SegNet pipelines), our engine reviews leaves down to the pixel level, tracking early fungal blight lesions and rust spots.

Multispectral satellite Earth observation bands

Satellite Analytics

We ingest Sentinel-2 and Landsat multispectral bands. By tracking red-edge and near-infrared reflectance index, IPS CeresAI estimates chlorophyll density and field stress maps on a weekly cycle.

PostGIS geospatial geometry index maps

Geospatial Intelligence

All measurements are mapped on a standardized EPSG coordinate plane. PostGIS-enabled database servers perform rapid geometry overlaps to coordinate yield histories with current plots.

Crop yield forecast modeling curves

Predictive Analytics

Time-series regression models run crop development curves. Ingesting cumulative daily temperature values, we project growth intervals and predict final yield weights within 6% deviation.

Serverless API compute container logs

Cloud & Data Infrastructure

Our serverless API endpoints scale dynamically based on request load. Data streams through Kafka queues into analytical storage pipelines, securing immediate response times.

Technical Stack Specifications

Layer Technologies Utilized Performance Target
Data Ingestion Apache Kafka, AWS Kinesis, gRPC Gateway < 200ms ingestion latency
Spatial Databases PostgreSQL, PostGIS, AWS Aurora Serverless Index execution < 50ms
AI Model Inference TensorRT, ONNX Runtime, AWS SageMaker Leaf image classification < 85ms
Drone Stitching GDAL, OpenDroneMap core containers 100-hectare orthomosaic < 4 hours