Visoar ag explorer: real-time aerial image stitching for precision agriculture

Overview

ViSOAR Ag Explorer addresses a critical bottleneck in precision agriculture by enabling real-time stitching of full-resolution aerial imagery directly in the field, without requiring an internet connection. Traditional crop scouting workflows require 15–20 minutes per 60-acre field, with visual inspections manually recorded into computers. This process introduces inconsistencies, memory limitations, and time constraints that reduce effectiveness.

ViSUS's scalable image stitching solution processes approximately 600 images in 10 minutes, producing a full-resolution mosaic view of the surveyed area. This enables instant analytics and actionable insights for farmers, allowing crop scouts to focus on ground truthing rather than initial discovery. The technology is designed to be cost-effective and deployable on commodity hardware, making it accessible for agricultural operations of varying scales.

Technical specifications

Key features:

• Progressive image stitching that solves 600 images in 10 minutes on standard commodity hardware
• Fully offline operation, eliminating dependency on internet connectivity in remote field locations
• Scalable architecture capable of handling massive aerial image mosaics
• Full-resolution output enabling detailed visual analysis of crop conditions
• Integration potential with artificial intelligence and Python scripting for advanced analytics
• Support for time-series dataset analysis for phenotyping and seasonal prediction tools

Technology readiness level

Initial technical feasibility has been demonstrated through real-time stitching of massive aerial image mosaics on commodity hardware. The solution is currently being validated through an active partnership with Lance Ramthun of Crop Solutions in Jonesboro, Arkansas, where 1,500–2,000 acres per week are being surveyed. Early results have shown improved visibility into planting, field preparation, weed presence, and intensity assessment compared to field-level observation.

Future validation efforts include collaboration with agricultural consultants during the off-season to develop phenotyping and prediction tools, as well as incorporating domain expertise to enhance AI-driven analytics capabilities.