The hardware-software stack that enables autonomous agricultural robotics to survive and thrive in the most demanding environments on earth.
Industry Standard
“Generic computer vision models trained on Western monocultures underperform on Indian fields by 15-30%.”
Swamikri Logic
Our models are trained exclusively on indigenous varieties and fragmented landholdings, achieving superior accuracy in variable light and high-dust conditions.
Latency
12ms
Inference Accuracy
99.2%
Real-time object detection and segmentation optimized for high-density planting and varied weed architecture.
On-device processing capability for deep neural networks without requiring satellite connectivity.
Technical documentation available
Request accessarrow_forwardOur proprietary dataset contains over 4 lakh annotated images specifically of South Asian soil profiles, crop phenotypes, and localized pests. This massive local corpus ensures our robots recognize the difference between a high-yield ‘Desi’ variety and a nutrient-deficient plant under the harsh Indian summer sun.
Standard industrial sensors fail in 45 °C heat and monsoon humidity. We engineer our enclosures using thermal-dissipating alloys and ultrasonic welding, surpassing IP64 standards for total dust ingress protection and prolonged immersion.
Sub-centimeter weed localisation using fused RGB and near-infrared imaging, trained on proprietary Indian crop-weed datasets across 8 farms.
Real-time meristem coordinate prediction pipeline that maps detected weed position to delta arm XY coordinates with <5mm strike accuracy at tractor operating speed.
Continuous field-level learning system that tracks weed species shifts season-over-season, auto-updating detection models to counter emerging herbicide-resistant strains without retraining from scratch.