Continuous intelligence at the sub-field level.
Multispectral drone imagery, satellite feeds, IoT soil moisture/nutrient/temp sensors, weather data, historical yield records.
Disease lesions 7–14 days early, zone-specific irrigation schedules, sub-field yield forecasts, nutrient deficiency maps.
CNN-based computer vision for spectral anomaly detection, time-series ML for forecasting, real-time edge inference, continuous cloud retraining.
High-resolution multispectral captures across 20+ spectral bands.
Continuous multi-temporal coverage for trend analysis and historical baseline establishment.
Soil moisture, NPK levels, temperature, and humidity recorded at 15-minute intervals.
Hyper-local forecast data fused with sensor readings to anticipate microclimate shifts.
Spectral anomaly detection on drone/satellite imagery. Detects disease signatures 7–14 days before visible symptoms. Outputs geo-tagged severity maps.
Fuses IoT sensor history with weather data and crop stage models. Generates zone-specific irrigation schedules and yield predictions updated daily.
Models are retrained each season on farm-specific outcomes. Accuracy compounds year-over-year as the system learns your specific soil and microclimate.
Edge to Cloud
Our ruggedized IoT sensor nodes capture soil moisture, temperature, humidity, and nutrient levels every 15 minutes. That data streams via cellular or satellite link directly into our cloud AI pipeline — no manual uploads, no delays, no missed signals.
The result: a farm manager gets an alert about a developing disease hotspot before it's visible to the human eye, with a map showing exactly which sub-field zones to treat first.
Global availability, edge-to-cloud synchronization, and hardware-accelerated AI.
We leverage AWS for secure backend API hosting and massive scale. Time-series sensor data streams into Amazon S3 and RDS, while SageMaker manages our model inference. Real-time SMS and app alerts are powered by Lambda and SNS. As operations expand across regions, CloudFront delivers high-resolution field maps globally with zero latency. Amazon Bedrock extends our AI capabilities as we expand.
Processing gigabytes of multispectral imagery requires serious compute. We use NVIDIA GPUs to accelerate both training and inference. CUDA and TensorRT enable real-time anomaly detection on farm imagery. Triton Inference Server handles our multi-model serving architecture, while Jetson edge modules power on-farm localized AI for environments with limited internet connectivity.